JP5854251B2 - Artificial intelligence device - Google Patents

Description

  The present invention sequentially learns problem solving techniques from input information and builds intelligence to solve problems autonomously. Information source, reliability, novelty, field, theme, subject, subject modification, predicate, predicate modification, modification relationship, sentence type, formula, chemical formula, symbol, when, where, who, what Analyze how, why, and why, and the relationship with the recorded related information, organize the information into a structure that is easy to search, and autonomously record information that is judged useful, Build a knowledge system with a relationship between them. In addition, the input information is evaluated against the knowledge system built internally, and processing (recording of information, updating / improvement of the knowledge system, execution of instructions, answering questions) is performed autonomously according to the evaluation results. To do. In addition, by generalizing some of the words included in the information and strengthening the relationship between the sequentially input information and the information as the relationship between the set of patterns corresponding to the information and concept, This is related to artificial intelligence, robots, and software that implements their functions to autonomously construct common sense, general ideas and problem solving methods from natural language from a series of input information.

When the machine performs processing of input information, etc., it is realized by incorporating and executing a program created in advance in a program language in a computer mounted on the machine. A program is created such that when a preset condition is detected, a corresponding operation is executed. If the detection of the condition and the corresponding action are not appropriate, the program installed in the computer is modified. Other conventional techniques include expert systems and artificial intelligence techniques using neural networks.
The expert system is a program composed of rules, and is composed of a fixed inference engine and a variable knowledge database. The inference engine performs inference using rule groups, and propositional logic, predicate logic, recognition logic, modal logic, temporal logic, fuzzy logic, etc. are used as rule groups as inference logic. A knowledge database is generally constructed by inputting expert knowledge, but when formulated, problems such as contradictions among rules have been pointed out in the literature.
In a neural network, artificial neurons in the input layer, intermediate layer, and output layer are generally connected serially, and the connection between the artificial neurons is performed by synaptic learning. As the synapse learning, an error back propagation method (back propagation) is generally used. However, back propagation has the following drawbacks pointed out in the literature.
-In backpropagation, the transfer function used in artificial neurons must be differentiable.
-Convergence in learning by backpropagation is very slow.
・ Back-propagation learning does not always converge.
・ In many cases, it converges to a local minimum error point instead of a global optimal solution. Conventionally, there is no artificial intelligence that learns problem solving methods from equally input information and autonomously solves problems. In addition, information source, reliability, novelty, field, theme, subject, subject modification, predicate, predicate modification, modification relationship, sentence type, formula, physical formula, chemical formula, symbol, when and where Analyzing who, what, how, why, and the relationship with the recorded related information, organizing and recording the information in a structure that is easy to search, and autonomously determining the information judged useful There has been no artificial intelligence, robot, and software that records and constructs as a knowledge system having a relationship between information. In addition, the input information is evaluated against the knowledge system built internally, common sense and general ideas are autonomously built from a series of input information, and input information about a series of problems and solutions. There has been no artificial intelligence, robot, and software that can autonomously generate solutions to similar problems from natural language.

Conventionally, when a machine performs processing of input information, etc., it has been necessary to set a program in the computer in advance. It was necessary to create a program for judging the situation from the input information and a program for causing the machine to operate according to individual conditions, and to implement and execute it on a computer mounted on the machine. The program had to be created in a dedicated programming language, and there was a demerit that it took a lot of time for development. It was difficult to evaluate the input information against the knowledge system built internally, and to construct common sense and general ideas from a natural language autonomously from a series of input information. In addition, it was difficult to answer clearly about the “symbol grounding problem” that the knowledge system built in artificial intelligence can be processed in conformity with reality.
In the present invention, processing for analyzing and recording information and the structure of information by human instruction and learning is performed, and the relationship between information is constructed as a knowledge system by connection between patterns and processing between patterns. Information source, reliability, novelty, field, theme, subject, subject modification, predicate, predicate modification, modification relationship, sentence type, formula, chemical formula, symbol, when, where, who, what Analyzing the relationship with the recorded related information, organizing and recording the information in an easily searchable structure, autonomously recording the information judged useful, and the knowledge system As a result, the input information is checked against the internal knowledge system and evaluated, and processing (recording of information, updating / improvement of the knowledge system, execution of instructions, answering questions) according to the evaluation results is autonomous. To implement. A part of words contained in information is generalized, and a significant relationship between sequentially inputted information and information is extracted by strengthening the relationship between the set of patterns corresponding to the information and the set of patterns, and a series of Autonomously construct common sense and general ideas from input information. It also realizes artificial intelligence, robots and software that autonomously generate similar problem solving methods from natural language input information about a series of problems and solutions. Furthermore, it solves the "symbol grounding problem" that the knowledge system constructed in artificial intelligence can be processed in conformity with reality, and realizes "artificial intelligence that does not run away". In addition to utilizing the constructed knowledge system for problem solving, the artificial intelligence that realizes the improvement of the knowledge system by autonomously starting the processing program necessary for problem solving and incorporating the processing results into the knowledge system is realized.

Human thought is expressed by language, numbers, mathematical formulas, symbols, and the like. In addition, since a language is expressed by a combination of words, human thought can be expressed by a combination of words, numbers, mathematical expressions, and symbols. When words, numbers, mathematical formulas, and symbols are converted into their corresponding patterns, individual human thoughts can be expressed as combinations of individual patterns, and human thought transitions can be viewed as changes from patterns to patterns. Can do.
Patterns can also express concepts like sentences and sentences. In addition, it is possible to sequentially excite related patterns and execute a number of processes on the excited patterns. It is also possible to start a processing program using pattern excitation as a trigger and to import the result into another pattern. Furthermore, it is a concept that has a very wide range that can be handled, for example, it is possible to generate a signal for driving the driving device in order to perform processing and operation of image information and information.
When patterns corresponding to words, numbers, mathematical expressions, and symbols are generated, various concepts can be expressed by combinations of these patterns. There are no restrictions on the format of words, numbers, mathematical expressions, and conversion from symbols to patterns as long as they can be identified. In the embodiment of the present invention, an identification number is assigned to each word. (For example, the word identification number of the word “I” is “0”, the word identification number of the particle “ha” is “1”, “teacher” “2”,... “Cube” “101”, “sphere” (“102”, “Density” “103”, “Weight” “104”...) Etc.) When a new word is generated, it can be dealt with by sequentially adding an identification number.
The recording unit unique to the present invention is used to express human thought or information as a pattern and to realize an autonomous transition from pattern to pattern. FIG. 21 shows the functional outline of the recording unit. The recording unit records various contents related to information. Examples of contents to be recorded include patterns that are information contents, connection information with other recording units (number of connections, connection destinations, relationships with connection destinations), conditions for exciting the recording units (matching conditions, Matching conditions), pattern analysis results (information source, reliability, novelty, field, theme, type of sentence), etc. As sentence types, ordinary sentences, question sentences, imperative sentences, conditional sentences, truth, facts, rules, common sense, definitions, logic, explanations, hypotheses, predictions, opinions, impressions, rumors, mathematical formulas, chemical formulas, symbols, etc. are identified. And record as analysis results. In addition, the pattern that is the content of the information is the subject, subject modification, predicate, predicate modification, modification relationship, sentence type, formula, physical formula, chemical formula, symbol, when, where, who, what, how, Analyze why and why, and the relationship with the recorded related information, and organize and record the information in a structure that is easy to search. When information is entered, the field of information, theme, sentence type, syntax (subject, subject modification, predicate, predicate modification, modification relationship, sentence type, formula, physical formula, chemical formula, symbol, when, where, Analyze who, what, how and why) and compare it with the recorded information, and the value of the information (reliability, novelty, validity, integrity, relationship with known information) ). As a result of the evaluation, valuable information is recorded in a recorder by organizing the relationship with the field, theme, and known information.
There are various relationships with known information such as cause and effect, event and reason, explanation and conclusion, summary and details, similar meaning, opposite opinion, text and summary. Setting of these relationships is performed by an autonomous setting by learning a human instruction or a relationship between information and information.
Since information is recorded as a knowledge system having a relationship with related information in this way, when certain information is input and the corresponding recording unit is excited, information on the related recording unit (connection destination, It is possible to retrieve the necessary information by calling up the appropriate recording units sequentially and calling up the relationship with the connection destination. Conventional artificial intelligence mainly takes a technique of searching related documents using a word included in information as a search key and selecting one having a large correlation with desired information. Therefore, even if there is an error in the information of the related document, there is a disadvantage that a document having a large correlation is output as an answer. In the present invention, the input information is incorporated into the knowledge system after evaluating the validity and consistency with the already evaluated knowledge system, so that erroneous information is just as well as improving human intelligence while receiving education. It is possible to build a knowledge system while eliminating
In addition, it is difficult to evaluate in detail the validity of the results output by artificial intelligence using conventional artificial intelligence technology (data retrieval, neural network). This is because, in data retrieval, correlation calculation and the like are performed through a complicated retrieval algorithm and the result is derived, so it is difficult to trace the situation in the form and logic understood by humans. A neural network also generates output results by strengthening the connection between a large number of artificial neurons by machine learning, but it is difficult to trace the learning process in a form, logic or language that can be understood by humans. On the other hand, in the present invention, all the information of each recording unit (matching and matching (excitation) conditions, patterns, connection relations between patterns, relations with connected patterns and pattern analysis results) is a language that can be understood by humans. , Can be expressed in symbols. In addition, since the recording unit having connection relation with each recording unit and the relation of connection are clear, it is possible to grasp the logical development. The artificial intelligence apparatus according to the present invention is a transparent system in which all processes are visible to humans and can be understood.
The knowledge system built inside is based on truth, facts, rules, common sense, definition, and logic, and information that does not match these is not taken into the knowledge system. The entire process can be displayed in a language or symbol that can be understood by humans, thus making the system transparent. In addition, regarding the “symbol grounding problem” that the knowledge system built in artificial intelligence can be processed consistently with the reality, the source of knowledge of this artificial intelligence device is the “language, sentence corresponding to the reality taught by humans”. , Sentences, symbols, mathematical formulas, physical formulas, chemical formulas "are converted into patterns and implemented as connection relations between patterns, and the built knowledge system is originally structured content that corresponds to reality. Therefore, it is essentially consistent with reality. Some artificial intelligence researchers said, “In cognitive science, we are conducting research on human intelligence with methods such as neuroscience, cognitive psychology, information science, and even cultural anthropology. The whole thing is unknown, so it is difficult to realize artificial intelligence equivalent to human intelligence. "Although there is an opinion that it does not fully understand the whole human intelligence mechanism, it corresponds to human intelligence We believe that artificial intelligence is possible. This artificial intelligence device sequentially inputs and evaluates human intelligence and knowledge as language, sentence, sentence, etc. information, and analyzes the relationship with information that has already been recorded as valuable information And autonomously incorporate it into the knowledge system. The knowledge system constructed as an organized form by evaluating the value of input information, adding analysis results to information judged to be valuable, is a database corresponding to human intelligence. This artificial intelligence device uses the constructed knowledge system to search related data related to a certain problem. The connection destination and the relationship can also be identified for the information related to the retrieved related data. By selecting the relevant ones in the processing process (thinking process) one after another, the relevant information is narrowed down to the appropriate one, and the thinking process proceeds autonomously toward the appropriate goal. It is possible to go. This artificial intelligence device autonomously generates the connection relationship between information and information by sequentially inputting information corresponding to truth, fact, rule, common sense, definition and logic. When information is input, a number of patterns corresponding to the information are excited, and the connection relationship between the pattern set (concept) and the pattern set (concept) is strengthened. Sentences and sentences do not exist alone, but are positioned within the context. In this artificial intelligence device, the corresponding pattern is excited according to the input sentence, and the history of the excited pattern is transferred to the connection relation recording part of the excited pattern before the pattern is excited. Connection relationships are strengthened sequentially. That is, learning proceeds so that all patterns corresponding to sentences are excited based on the relationship with related sentences, that is, context. When such information is connected to information corresponding to truth, facts, rules, common sense, definition, and logic, the knowledge system corresponding to truth, facts, rules, common sense, definition, and logic is autonomous. Can be generated. As learning proceeds, the artificial intelligence can identify whether the input information is consistent with the information already recorded, but not. In other words, it has acquired knowledge and intelligence equivalent to truth, facts, rules, common sense, definition and logic.
In the following, differences from the conventional artificial intelligence technologies such as expert systems, neural networks, finite automata, and programming languages for artificial intelligence will be described.
The expert system is a program composed of rules, and is composed of a fixed inference engine and a variable knowledge database. The inference engine performs inference using rule groups, and propositional logic, predicate logic, recognition logic, modal logic, temporal logic, fuzzy logic, etc. are used as rule groups as inference logic. A knowledge database is generally constructed by inputting expert knowledge, but when formulated, problems such as contradictions among rules have been pointed out in the literature.
On the other hand, the processing in the present invention is not fixed like a program, and the processing is expressed by a change from pattern to pattern. The process can be changed by changing the connection between patterns. In the expert system, only logical expansion (propositional logic, predicate logic, recognition logic, modal logic, temporal logic, fuzzy logic, etc.) by inference using rule groups is the object of processing, but in the present invention, processing other than logical expansion is performed. (Relevance definition, novelty detection, evaluation, etc.) are also possible, making the system more flexible than the expert system. In addition, while the expert system is a half-data driven system such as an inference engine (fixed) and a knowledge database (variable), in the present invention, functions and processes can be handled as data, and a complete data driven system. It has become. Since an expert system is a program composed of a group of rules, it is also subject to program language restrictions. Since the present invention is a complete data driven system that can handle data in terms of processing, it is possible to easily change or add processing during execution of processing. This is performed by changing the connection between the pattern expressing the processing and the pattern. Normally, in the case of programming processing in a programming language, reprogramming and recompilation are required when processing changes are implemented. However, in the present invention, processing changes, additions, etc. are made by changing the connection relationship between corresponding patterns locally. Since it can be implemented by changing, there is no need to rebuild the entire system like recompilation. (This can be handled by changing the contents recorded in the recording unit.)
Next, differences from the finite automaton will be described. A finite automaton has a restriction that only one state can be obtained at a certain point in time. Due to this restriction, it is difficult to perform an operation of simultaneously taking a plurality of states that can be realized in the present invention. Of human thoughts, it is reasonable to express what corresponds to consciousness by the action of taking one state (excited thoughts express actions that suppress other thoughts), but it corresponds to human unconsciousness. As an expression of the action, the restriction that only one state can be taken is considered inappropriate. (Because unconscious thinking has weak suppression between thoughts, and multiple thoughts can operate in parallel) In the present invention, it is not necessary to constrain pattern excitation to one, so it is like a finite automaton. There are no major restrictions. (Thinking corresponding to consciousness and unconsciousness can be expressed as a pattern.) Thinking corresponding to unconsciousness is assumed to correspond to the previous thinking that becomes explicit like thoughts corresponding to consciousness. Moreover, parallel processing by a plurality of pattern excitations is possible. (The problem of interference between patterns in parallel processing can be solved by separating the processing lines.) In addition, there are cases where there are a plurality of solutions to a certain problem instead of one. Without forcibly narrowing down to one way, it is possible to perform an operation by performing parallel processing and finally selecting the optimum one.
Hereinafter, differences from the neural network will be described. In the present invention, the connection between the recording units can be handled very flexibly. Patterns and patterns can be connected by specifying them individually. Any combination such as many-to-many coupling, multi-stage coupling, feedforward coupling, feedback coupling, etc. can be specified and set. On the other hand, in a neural network, artificial neurons in the input layer, intermediate layer, and output layer are generally connected in series, and the connection between artificial neurons is performed by synaptic learning. As the synapse learning, an error back propagation method (back propagation) is generally used. However, back propagation has the following drawbacks pointed out in the literature.
-In backpropagation, the transfer function used in artificial neurons must be differentiable.
-Convergence in learning by backpropagation is very slow.
・ Back-propagation learning does not always converge.
・ In many cases, it converges to a local minimum error point instead of a global optimal solution. On the other hand, the present invention does not have the above-mentioned drawbacks because it does not use an iterative optimum value search method such as backpropagation for connection between recording units.
Finally, the comparison with the programming language for artificial intelligence is shown. In the present invention, basic processing (pattern search, collation, comparison, difference extraction, recording, output, arithmetic processing between patterns, etc.) is assumed to be implemented in a programming language, but corresponds to individual thoughts. As for the contents of processing, it is a data driven system that analyzes patterns and performs control according to the analysis results, so it is not necessary to individually program. This system evaluates input information (language) and proceeds with processing according to the evaluation result. Accordingly, it is possible to easily change or add a process during the execution of the process. Normally, in the case of programming processing in a programming language, reprogramming and recompilation are required when processing changes are implemented. However, in the present invention, processing changes, additions, etc. are made by changing the connection relationship between corresponding patterns locally. Since it can be implemented by changing, there is no need to rebuild the entire system like recompilation. In programming languages, the names of variables, constants, functions, etc. used in the program will not work unless they are strictly specified. In contrast, in the present invention, each piece of information is expressed as a pattern corresponding to a natural language, and processing is performed by connecting the patterns. Therefore, there is some ambiguity such as enabling processing with similar patterns. However, it is possible to construct a system that operates. In other words, it is possible to construct a system that can be processed even if there is some ambiguity and that is closer to human thought processing. (For information retrieval, natural language can be used to detect sentences with the same meaning, detect novelty, check consistency with recorded information, extract differences, etc.)
The artificial intelligence apparatus of the present invention covers not only the functions of the expert system, finite automaton, neural network, and programming language for artificial intelligence that are used in conventional artificial intelligence, robots, etc., but also functions that they do not have And flexibility (complete data driven system, flexibility of function expansion (function can be added by local change), support for systems that allow ambiguity, etc., unconscious (multiple state) simulation functions, parallel processing This system has compatibility, variety of connections (feedforward, feedback, many-to-many coupling, etc.), no restriction of differentiability, and no convergence problem in backpropagation.
Human thought can be broadly classified into two. One is the content of thought, and the other is the way of thinking (procedure, process, way of thinking). In the embodiment of the present invention, the content of thought is treated as a pattern obtained by converting natural language, and the function is realized by setting the processing method by a program for the way of thinking. Though the content of thinking is diverse, the way of thinking is common and general. For example, a process corresponding to the type of sentence (normal sentence, question sentence, command sentence, conditional sentence) can be set as the process of input information. If it is a normal sentence, the reliability and validity of the input information will be evaluated, and if it is valued, it will be incorporated into the knowledge system. If it is an interrogative sentence, related information is searched from the recorded knowledge system, and if there is appropriate information, it is output as an answer. If it is a command sentence, the presence / absence of experience of executing the same command in the past, the execution procedure, and the effect of the execution will be evaluated to determine the response. If it is a conditional statement, the establishment of the content corresponding to the condition is analyzed from the knowledge system or external information, and processing according to the analysis result is performed. By implementing a processing method with a program corresponding to the way of thinking described above, it is possible to process a wide variety of thought contents in a natural language. Therefore, very flexible and scalable artificial intelligence can be realized at a very low development cost. (In the conventional artificial intelligence, it is necessary to implement a part corresponding to the content of thinking by a program, and a large development cost is required to realize each function.) The recording unit shown in FIG. 21 operates this artificial intelligence. The above shows that the transition from information (thinking, concept) to information (thinking, concept) can be realized by using the relationship between information. When thinking, humans use relationships (causes and consequences, events and reasons, explanations and conclusions, outlines and details, similar content, opposition opinions, etc.) to change how they think. Therefore, this artificial intelligence apparatus can also transition from information (pattern) to information (pattern) using the relationship between patterns. When certain information is entered, the related information is diverse, but the relevant information has an appropriate relationship (cause and result, event and reason, explanation and conclusion, summary and details, similar content, opposite) It is possible to shift the thinking step to an appropriate next step according to the way of thinking.
There are three main methods for exciting the recording unit. The first method is to irradiate each recording unit with a search pattern from the pattern controller to excite the correlated recording units. The second is a method of irradiating the excitation history in the set period from the pattern irradiator to the connection information recording unit of each recording unit to excite the recording unit that satisfies the excitation condition. The third method is to excite a recording unit having an appropriate relationship by using connection information (number of connections, connection destination, relationship with connection destination) recorded in the recording unit. . In this artificial intelligence apparatus, these excitation methods are appropriately used in each process to excite the most appropriate recording unit for transition of the thinking pattern.
This artificial intelligence device also has an autonomous learning function. Corresponding recording units are sequentially excited by information input from the outside, and this excitation history is recorded in a place called a pattern irradiator. As each recording unit is excited, a part of the excitation history (excitation history for a certain period going back to the past from the time of excitation) is transferred to the connection information recording section of the excited recording unit. The connection relationship with the recording unit, which is considered to be strongly related to excitation, is gradually strengthened. When the input information is learning data, the learning data are sequentially input, thereby strengthening the relationship between the learning data (logical relationship, causal relationship, time series relationship, etc.). When a part of information is input after the learning is completed, learning data related to the information is sequentially excited, and the learned content is automatically recalled. In each step, a plurality of recording units can be excited simultaneously.
In the present invention, instead of individually programming the processing of input information, a method for analyzing the relationship between information and information in a machine, a method for constructing a knowledge system and a method for solving problems, a method for generalizing input information, etc. Implemented by implementing a method of processing input information. It interprets information expressed in language, numbers, mathematical formulas, and symbols, and autonomously performs appropriate processing according to the interpretation results. Conventionally, the content of the processing was expressed by a program. However, in the present invention, the processing of the next step is performed by searching related information expressed in a natural language, collating with a condition, performing processing according to the condition, and branching. Since it is performed autonomously, it is not necessary to program the contents of each process, and it is sufficient to indicate the contents of the process in a natural language. In other words, it is not necessary to program each process sequentially, and knowledge already expressed in natural language can be used as a source of knowledge system of the artificial intelligence device. (By sequentially inputting knowledge expressed in natural language, the artificial intelligence device analyzes and organizes the relationship between information and incorporates it into the knowledge system.)
Information source, reliability, novelty, field, theme, subject, subject modification, predicate, predicate modification, modification relationship, sentence type, formula, chemical formula, symbol, when, where, who, what Analyze how, why, and why, and the relationship with the recorded related information, organize the information into a structure that is easy to search, and record truth, truth, facts, expertise, rules, and common sense Is constructed as a knowledge system with a relationship between information.
Initially, a human teaches the processing method, and the machine records and learns the method taught by the human. As learning progresses to some extent, the machine will autonomously execute processing. A human confirms the processing result of the machine, and if the process is incorrect, notifies the machine and corrects the process appropriately. Learning is performed by specifying the relationship between patterns. By specifying the relationship between concepts expressed in natural language, the machine automatically generates a connection relationship as a relationship between patterns. The human thought process can be generally divided into the content of thought and the way of thinking. Thought content varies widely, the way of thinking has general, universal, and common features. In the present invention, a thought process is shifted in an appropriate and significant direction by controlling a pattern corresponding to the content of thought with a pattern or program corresponding to the way of thinking. The relationship between information and information is not a one-to-one relationship but a many-to-many relationship. However, the relationship can be generalized and associated. For example, relationships can be identified as cause and effect, event and reason, explanation and conclusion, summary and details, and so on. In the transition of the thinking pattern, it is possible to efficiently reach the target by selecting an appropriate one of various ways of thinking and the relationship between information and information according to the thinking stage. Thus, in the past, it was necessary to individually program a wide variety of processes, but the part corresponding to the various processes was set in natural language, and the part related to the function of selecting the way of thinking and the relationship between information By implementing it programmatically, the labor required for development can be greatly reduced. The function of selecting the way of thinking and the relationship between information can also be realized by processing between patterns.

By converting the language that expresses human thought into patterns and analyzing the relationships between patterns and patterns, sentence types (normal sentences, question sentences, imperative sentences, etc.), features (truth, truth, facts, definitions, rules, Common sense, explanations, hypotheses, predictions, opinions, impressions) and relationships (causes and consequences, events and reasons, explanations and conclusions, outlines and details, etc.) can be identified. Sentence types can be trained by machines so that humans can identify the types and characteristics of sentences. The sentence type can be identified as a normal sentence, a question sentence, a command sentence, a conditional sentence, etc. by analyzing words included in the sentence. Among the features of the sentence, truth, truth, facts, definitions, rules, and common sense are taught by humans for each individual and learned by the machine. This can be done by identifying each information type (truth, truth, fact, definition, rule, common sense) and recording it with additional information when each information is input to the machine. Identification of other types of information (explanation, hypothesis, prediction, opinion, impression) is that the sentence type is not recorded as (truth, truth, fact, definition, rule, common sense) and the word ( I think, think, think, etc.) can be implemented by analyzing.
If the language is converted into a pattern and expressed as a set of patterns corresponding to the words that make up the sentence, and the semantic relationship between words is expressed by the connection relationship between the patterns, the meaning of the sentence is expressed between the patterns and the patterns. It can be expressed as a connection relationship. Input information includes word identification from word strings, word attributes (part of speech, meaning), sentence element analysis, sentence structure analysis (subject, predicate, subject modification, predicate modification, modification relationship), sentence elements and Sentence element relationship analysis (same meaning, definition, opposite meaning, etc.) is performed, and the relationship between the sentence element and the sentence element is associated as a connection relationship between the pattern and the pattern, so that the relationship between the information and the meaning of the information It will be converted into a connection relationship. The relationship between patterns can be set not only for sentence elements and sentence elements but also for sentences and sentences, and sentences and sentences. This can be done by defining a group of patterns as a new pattern. When a characteristic word indicating a pattern-pattern relationship (cause and result, event and reason, explanation and conclusion, outline and details, etc.) is detected, the corresponding relationship between the corresponding patterns is recorded autonomously. This relationship between recorded patterns can be utilized when controlling transitions between patterns.
The connection relation between patterns can express various relations such as logical relation, definition, attribution relation, similar relation, relation between action and result, inference development, inheritance of attributes, common Feature inheritance and identification of individual features can also be expressed flexibly.
By setting the information-information relationship as a pattern-to-pattern connection relationship in this way, information is not recorded as a single state, but as a knowledge system that has relationships with other information. It becomes possible.
The relationship between information has various relationships, but the relationship between information (same meaning, definition, opposite meaning, similarity, logic, cause, result, detail, summary, summary, related information, etc.) By selecting an appropriate one in each processing phase (corresponding to the stage of the thought process), it is possible to appropriately control the pattern transition from pattern to pattern.

When information is input, a meaning is analyzed from words included in the information, and a pattern indicating a meaning equivalent to the information is generated. This pattern is generated while maintaining the sentence subject, predicate, subject modification, predicate modification, and modification relationship. The generated pattern is used for collation with the recorded pattern, and an analysis is performed as to whether or not there is a related pattern. Since information can be searched at the semantic level in this way, it is possible to search for information that is related in meaning and content even if the words do not match.
The newly entered information can be checked against the already recorded knowledge system and evaluated for consistency and novelty. If it is inconsistent with the information identified and recorded as truth, truth, fact, definition, rule and common sense, the information is likely to be incorrect. On the other hand, if it is not possible to determine whether the information is consistent or not, it is considered that knowledge sufficient to determine the information has not yet been accumulated, so a human being is notified and a determination is made. If humans can judge that the information is correct, the result will be notified to the machine. The machine recorded the information as an explanation or hypothesis, and obtained another information in the future, and logically developed it with a combination of truth, truth, facts, definitions, rules and common sense, and was able to confirm consistency and validity Sometimes it will be identified and recorded as highly reliable information evaluated.
In this way, information is converted into patterns, information types and characteristics are identified, classified and recorded, and information-information relationships (logical relationships, similar relationships, reciprocal relationships, analogies, etc.) Information can be constructed as a knowledge system by expressing the connection relationship between patterns.

If information is constructed as a knowledge system, problem solving using the knowledge system becomes possible. First, the problem is analyzed using the knowledge system, the problem is clarified, and the goal of problem solving is set. Next, a solution plan for solving the problem is extracted and applied. The situation after application is evaluated, and if the goal of problem solving is reached, the process is completed. If the problem solving goal has not been reached, the above process is applied again to the new state to bring it closer to the problem solving goal. By instructing these processes in a natural language, this machine converts the connection relationship between patterns and executes the process.
The problem analysis and problem clarification are also implemented by learning how to detect and identify the difference between the target state and the current situation. Moreover, the solution corresponding to each problem is implemented by learning the corresponding process. Learning is performed by recording a history of pattern transitions. The content taught by humans is recorded as a transition from pattern to pattern.
As learning progresses, the taught process is autonomously executed as a transition from pattern to pattern, and the taught content is executed.
The transition of human thought patterns can be generally expressed as a conditional process from the viewpoint that the transition destination changes depending on the condition. In the present invention, the meaning of the language is interpreted and autonomously converted into conditional processing as necessary. The condition of the conditional processing is determined by generating a search pattern from the corresponding language and searching autonomously. Judgment is made as to whether or not the retrieved information satisfies the condition, and if satisfied, the corresponding processing is executed. Even if a problem solution that humans use as knowledge is input as a language as it is, the meaning is sequentially interpreted and conditional processing proceeds autonomously to solve the problem. When new information is required during the process, the information request is notified, and when appropriate information is acquired, a process corresponding to the content is performed.
In the learning phase, when a pattern is excited, the history of the pattern excited in the set period before the pattern is excited is referred to, and the connection relationship with the excited pattern is strengthened. In the practical phase, a part of the recorded excitation history data is used to check whether the excitation condition recorded in each recording unit is met, and when the excitation condition is met, the corresponding pattern is excited. When the pattern is excited, the history of the excitation pattern is updated, the above operation is repeated in a new state, and the pattern that matches the excitation condition is sequentially excited.

In the learning phase, a series of patterns corresponding to processing is sequentially excited by a human instruction, and by recording the history, the corresponding pattern is excited according to the instructed procedure. The behavior of the pattern is not static but shows dynamic behavior. The necessary information is searched using the internal pattern recorded in the pattern, and the search result is stored in the required place. Also, processing such as conversion of the internal pattern recorded in the pattern into a designated arrangement is performed. Numerous processes (formula processing, chemical formula processing, translation, etc.) can be performed by combining patterns having such dynamic behavior.
Teaching to this machine can be performed by inputting natural language sequentially without programming. The input language information is analyzed for syntax, meaning, and relationship with already recorded information, and the corresponding pattern is excited according to the analysis result, and processing is executed. Numerous processes such as value evaluation and recording of input information, execution of instructed instructions, and generation of solutions to problems / issues are possible.
The overall operation of this machine is managed by the pattern controller. In each pattern transition cycle, information input, information analysis (sentence type, syntax, meaning, etc.), information evaluation (novelty, reliability, validity, usefulness, etc.), information processing (problem / problem solution) Generation, recording, information output, etc.).

Next, generalization of input information will be described.
Feature extraction and generalization of a part of a word included in an input sentence For example, proper nouns appearing in sentences are generalized as person A, person B, object C, object D. Sequentially, sentences are converted into patterns, and the converted patterns are excited. At this time, the generalized pattern is also sequentially excited. By inputting a lot of information, the connection between specific patterns constituting a sentence is strengthened. The input sentence has a relationship with a nearby sentence. Since this relationship is expressed by the relationship between words constituting a sentence, the same relationship is often expressed by a combination between the same words or similar words. Each time a sentence pattern is excited, words in the sentence are excited. By generalizing proper nouns, etc., the relationship between generalized persons and persons or objects will be strengthened. Sentences appear more frequently than when expressed with proper nouns. With this effect, the relationship between objects that does not depend on proper nouns is extracted. Further, since the feature extraction of words extracts not the relationship between individual words but the relationship between word features, generalization from the feature side becomes possible. This relationship can be detected because the connection relationship between specific patterns is strengthened. In particular, relationships strengthened from many sentence examples have generality, and those corresponding to common sense or general ideas are extracted. This common sense or general idea depends on the text group to be entered. That is, when a sentence group corresponding to a certain culture is input, common sense or a general idea corresponding to the culture is extracted. When a group of sentences in the same culture is input, the way of thinking is considered to be equivalent. Therefore, the way of thinking strengthened by many people in the same culture is extracted.
Similarly, by extracting and generalizing the characteristics of some words for a number of problems and their solutions and operating them, a generalized relationship between the problems and their solutions can be extracted. As learning by information input progresses, this machine can autonomously generate a solution to a similar problem.

In the following, description will be given of the operation of recording information in the pattern recorder in a state in which information is identified as a conditional part and a processing part and organized as a sentence structure. By analyzing the information word, the condition part and the processing part of the information can be identified. For example, in the statements “A”, “GA”, “B”, “NO”, “C”, “do it”, [“A”, “GA”, “B”, “NO” ”is the condition part, and [“ C "Perform""is a processing unit. Also, in the sentence “A” “ga” “B” “if” “C” “ha” “D” “is” [“A” “ga” “B” “if”] is the condition part, [“C” “is” “D” “is”] is a processing unit.
In this way, the sentence is divided into a conditional part and a processing part, and converted into a structure having inter-pattern connection for conditional processing. Further, when the pattern of the condition part is excited, a process for autonomously confirming whether or not the condition part is established is excited. When a sentence is converted into a pattern with a conditional processing structure in this way, an active thinking action is realized in which sentences that can be expressed by conditional processing proceed sequentially while confirming the validity of the condition. be able to. In general, human problem solving and action determination can be expressed by conditional processing. By inputting human problem-solving and action-determining measures in natural language, this machine autonomously converts it into conditional processing, and then proceeds with the processing while confirming the validity of the conditions. Operation is possible. By inputting knowledge (procedures and thinking methods related to problem solving and action determination) expressed in natural language without programming the actions corresponding to human problem solving and action determination, human beings can solve problems or make action decisions through thinking. As a result, problem solving or action determination can be carried out autonomously.

  FIG. 1 is a diagram showing a configuration of an artificial intelligence apparatus according to an embodiment of the present invention. In FIG. 1, reference numeral 1 denotes a pattern converter that converts information into a pattern. Reference numeral 2 denotes a pattern recorder composed of a recording unit for recording conditions for matching and matching (excitation), patterns, connection relationships between patterns, relationships with patterns having connection relationships, and pattern analysis results. 3 is the source of information, reliability, novelty, field, theme, subject, subject modification, predicate, predicate modification, modification relationship, when, where, who, what, how , Why, why, sentence type (common sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, It is a pattern analyzer that analyzes the relationship between chemical formulas, symbols, and recorded related information, organizes the information into a structure that can be easily searched, and records the information in a pattern recorder. 4. Matching and matching (excitation) conditions, patterns, connection relations between patterns and relations with patterns with connection relations are registered or changed by human instructions or autonomously, according to the pattern status and analysis results It is a pattern controller that performs control. Reference numeral 5 denotes a pattern inverse converter for converting a pattern into information. 6 records the excitation history of the pattern, uses the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied, and records that satisfy the matching (excitation) conditions It is a pattern irradiator that excites the unit.

Next, the operation will be described.
In FIG. 1, a pattern converter 1 converts information into a pattern. The converted pattern is analyzed by the three pattern analyzers, and processing according to the analysis result is performed. The second pattern recorder records the matching and matching conditions, the pattern, the connection relationship between the patterns, and the relationship with the pattern having the connection relationship. The input pattern is collated with the recording unit of the pattern recorder, and it is confirmed whether or not the related pattern is recorded. If the same or equivalent pattern as the input pattern is recorded, the corresponding pattern is excited, and if not recorded, it is registered as a new pattern and excited. The history of the excited (matched) pattern is recorded in the pattern irradiator 6. When a certain recording unit is excited, data on the connection relation with the pattern is generated from the history data of the pattern excited before that, and recorded in the connection relation recording unit of the recording unit of the pattern. Further, a part of the recorded excitation history data is used to check whether the excitation condition recorded in each recording unit is met, and the recording unit that matches the excitation condition is excited. In the initial stage, patterns and patterns are generated by teaching from humans.

Human thought is expressed by language, numbers, mathematical formulas, symbols, and the like. In addition, since a language is expressed by a combination of words, human thought can be expressed by a combination of words, numbers, mathematical expressions, and symbols. When words, numbers, mathematical formulas, and symbols are converted into corresponding patterns, individual human thoughts can be represented as individual patterns, and human thought transitions can be viewed as changes from patterns to patterns. .
Patterns can also express concepts like sentences and sentences. In addition, it is possible to sequentially excite related patterns and execute a number of processes on the excited patterns. It is also possible to start a processing program using pattern excitation as a trigger and to import the result into another pattern. Furthermore, it is a concept that has a very wide range that can be handled, for example, it is possible to generate a signal for driving the driving device in order to perform processing and operation of image information and information.
In the present invention, instead of individually programming the processing of input information, a method for analyzing the relationship between information and information in a machine, a method for constructing a knowledge system and a method for solving problems, a method for generalizing input information, etc. Implemented by implementing a method of processing input information. It interprets information expressed in language, numbers, mathematical formulas, and symbols, and autonomously performs appropriate processing according to the interpretation results. Conventionally, the content of the processing was expressed by a program. However, in the present invention, the processing of the next step is performed by searching related information expressed in a natural language, collating with a condition, performing processing according to the condition, and branching. Since it is performed autonomously, it is not necessary to program the contents of each process, and it is sufficient to indicate the contents of the process in a natural language.
In addition, information source, reliability, novelty, field, theme, subject, subject modification, predicate, predicate modification, modification relationship, sentence type, formula, chemical formula, symbol, when, where, who Analyze what, how, why and why, and the relationship with the recorded related information, organize and record the information in a structure that is easy to search, as well as truth, truth, facts, expertise, rules and Build common sense as a knowledge system.
Initially, a human teaches the processing method, and the machine records and learns the method taught by the human. As learning progresses to some extent, the machine will autonomously execute processing. A human confirms the processing result of the machine, and if the process is incorrect, notifies the machine and corrects the process appropriately. Learning is performed by specifying the relationship between patterns. By specifying the relationship between concepts expressed in natural language, the machine automatically generates a connection relationship as a relationship between patterns. The human thought process can be generally divided into the content of thought and the way of thinking. Thought content varies widely, the way of thinking has general, universal, and common features. In the present invention, a thought process is shifted in an appropriate and significant direction by controlling a pattern corresponding to the content of thought with a pattern or program corresponding to the way of thinking. The relationship between information and information is not a one-to-one relationship but a many-to-many relationship. However, the relationship can be generalized and associated. For example, relationships can be identified as cause and effect, event and reason, explanation and conclusion, summary and details, and so on. In the transition of the thinking pattern, it is possible to efficiently reach the target by selecting an appropriate one of various ways of thinking and the relationship between information and information according to the thinking stage. Thus, in the past, it was necessary to individually program a wide variety of processes, but the part corresponding to the various processes was set in natural language, and the part related to the function of selecting the way of thinking and the relationship between information By implementing it programmatically, the labor required for development can be greatly reduced. The function of selecting the way of thinking and the relationship between information can also be realized by processing between patterns.

FIG. 2 shows an example of a pattern. The input sentence is organized into a main part (subject and subject modification) and a predicate (predicate and predicate modification). The predicate modification further organizes what, when, where, why, and how. Organizing and storing in this way is very useful for retrieving information. The input word pattern is stored as an in-sentence pattern. Relationships between words are analyzed, sentence elements (subjects, predicates, modifiers) and modifier relationships are analyzed.
FIG. 3 shows an operation in which words, parts of speech / meanings, sentence elements, relations between sentence elements, and modification relations are sequentially identified from a word string. When a word is input, a recording unit corresponding to (matching) the input word is excited in the word detection area. When the recording unit corresponding to the word is sequentially excited, the word corresponding to the word string is detected and the recording unit corresponding to the word is excited. In the connection relation recording unit of the recording unit corresponding to each word, since the connection relation with the word string corresponding to each word is generated, the correlation is large when the history of the word string is irradiated in the pattern irradiator. Is detected and the recording unit is excited. When the word pattern is excited, the part-of-speech / meaning pattern of the word corresponding to the word is excited. Next, the sentence element and the modification relationship between the sentence elements are detected according to the appearance pattern of the word part of speech. If there are multiple words to be modified, they are located apart from the word to be modified, and it is difficult to identify which word is modifying which word by order of part of speech, the identification is also performed using the meaning of the word To do. (It can be identified by detecting the feasibility of the combination of the word to be modified and the word to be modified.) When the relationship between the sentence elements (subject, predicate, modification relationship) is identified, the relationship between the sentence elements Is recorded. Sentences entered from this relationship are organized into a main part (subject and subject modification) and a predicate (predicate and predicate modification). The data is stored in a structured state in the recording unit as an in-sentence pattern.

Next, how the input sentence is analyzed and recorded as an in-sentence pattern in the recording unit will be described.
FIG. 4 shows an example of current affairs. A procedure for analyzing a text example by the processing of FIG. 3 is shown. When a word is detected, the part of speech and meaning of the word are analyzed at the same time. Nouns, verbs, adjectives, adjective verbs, adverbs, particles, etc. are identified as parts of speech. Subject identification and modifier identification are performed based on the type of noun and particle. If there are multiple words to be modified, they are located apart from the word to be modified, and it is difficult to identify which word is modifying which word by order of part of speech, the identification is also performed using the meaning of the word To do. It can be identified by detecting the feasibility of the combination of the word to be modified and the word to be modified. By tracing the modification relationship sequentially, the modification of the subject and the modification part of the predicate can be identified. What is (O), when (H1), where (H2), why (H3), and how (H4) is implemented as a modifier of the predicate. This can be implemented by identifying the particle of each sentence element, and identifying whether it is in the middle of a modification relationship or a delimiter between sentence elements of each modification. Through the analysis described above, subject modification, subject, predicate modifier (what, when, where, why, how) and predicates can be identified. This analysis result is defined as the connection relation of the in-sentence pattern, and is recorded as the in-sentence (word) pattern connection information in FIG. By performing such an analysis, it is possible to strictly interpret a sentence that is a compound sentence (a sentence in which a sentence exists and a word is modified). There are usually multiple subjects, predicates, and modifiers in a sentence, but it is important to identify exactly which subject and predicate are the main and what others are modifying. it can. By performing this analysis on the input sentence, the relation between sentences can be strictly identified. Various comparisons (formal comparison, semantic comparison, designation of comparison location, etc.) can be made by comparing sentence subjects, predicates, and modifiers. Also, specify what information (what, who, what, when, where, why, how, and what you want to search) when searching for information from sentences recorded in the past. You can search for the information you want directly.

Next, a method for searching for desired information will be described.
The question sentence is converted into the form of subject, subject modification, predicate, predicate modification (what, when, where, why, how) as described above. At this time, information about the part corresponding to the question
A search pattern to be generated can be generated. In addition, the search pattern is set as to what sentence element is expected as the answer to the question (subject, subject modifier, predicate, predicate modifier). This
The search pattern generated from the above is irradiated onto the pattern recorder, and a pattern having a correlation is searched.
If there is, it becomes a candidate for an answer. In the case of compound sentences, the hierarchy of predicates corresponding to the questions is important. In other words, it is important to extract the answer from the hierarchy in which the predicate corresponding to the question is excited when the search pattern is irradiated and the related pattern is searched. When multiple predicates exist in a compound sentence, it is necessary to identify which predicate is expected to be answered.

  By converting the language that expresses human thought into patterns and analyzing the relationships between patterns and patterns, sentence types (normal sentences, question sentences, imperative sentences, conditional sentences, etc.), features (truth, truth, facts, definitions) , Rules, common sense, explanations, hypotheses, predictions, opinions, impressions) and relationships (causes and consequences, events and reasons, explanations and conclusions, outlines and details, etc.). Sentence types can be trained by machines so that humans can identify the types and characteristics of sentences. The sentence type can be identified as a normal sentence, a question sentence, a command sentence, a conditional sentence, etc. by analyzing words included in the sentence. Among the features of the sentence, truth, truth, facts, definitions, rules, and common sense are taught by humans for each individual and learned by the machine. This can be done by identifying each information type (truth, truth, fact, definition, rule, common sense) and recording it with additional information when each information is input to the machine. Identification of other types of information (explanation, hypothesis, prediction, opinion, impression) is that the sentence type is not recorded as (truth, truth, fact, definition, rule, common sense) and the word ( I think, think, think, etc.) can be implemented by analyzing.

If the language is converted into a pattern and expressed as a set of patterns corresponding to the words that make up the sentence, and the semantic relationship between words is expressed by the connection relationship between the patterns, the meaning of the sentence is expressed between the patterns and the patterns. It can be expressed as a connection relationship. Input information includes word identification from word strings, word attributes (part of speech, meaning), sentence element analysis, sentence structure analysis (subject, predicate, subject modification, predicate modification, modification relationship), sentence elements and Sentence element relationship analysis (same meaning, definition, opposite meaning, etc.) is performed, and the relationship between the sentence element and the sentence element is associated as a connection relationship between the pattern and the pattern, so that the relationship between the information and the meaning of the information It will be converted into a connection relationship. The relationship between patterns can be set not only for sentence elements and sentence elements but also for sentences and sentences, and sentences and sentences. This can be done by defining a group of patterns as a new pattern. When a characteristic word indicating a pattern-pattern relationship (cause and result, event and reason, explanation and conclusion, outline and details, etc.) is detected, the corresponding relationship between the corresponding patterns is recorded autonomously. The relationship between the recorded patterns is utilized when controlling the transition between patterns.
The connection relation between patterns can express various relations such as logical relation, definition, attribution relation, similar relation, relation between action and result, inference development, inheritance of attributes, common Feature inheritance and identification of individual features can also be expressed flexibly.
By setting the information-information relationship as a pattern-to-pattern connection relationship in this way, information is not recorded as a single state, but as a knowledge system that has relationships with other information. It becomes possible.

The relationship between information has various relationships, but the relationship between information (same meaning, definition, opposite meaning, similarity, logic, cause, result, detail, summary, summary, related information, etc.) By selecting an appropriate one in each processing phase (corresponding to the stage of the thinking process), it is possible to appropriately and significantly control pattern transition from pattern to pattern.
When information is input, a meaning is analyzed from words included in the information, and a pattern indicating a meaning equivalent to the information is generated. This pattern is generated while maintaining the sentence subject, predicate, subject modification, predicate modification, and modification relationship. The generated pattern is used for collation with the recorded pattern, and an analysis is performed as to whether or not there is a related pattern. Since information can be searched at the semantic level in this way, it is possible to search for information related to the contents even if the words do not match.
The newly entered information can be checked against the already recorded knowledge system and evaluated for consistency and novelty. Information that is identified as truth, truth, facts, definitions, rules and common sense and is not consistent with the information recorded is likely to be incorrect. On the other hand, if it is not possible to judge whether the information is consistent or not, it is considered that knowledge sufficient to determine the information has not yet been stored, and therefore human judgment is required. If humans can judge that the information is correct, the result will be notified to the machine. The machine records the information as an explanation or hypothesis, and obtains other information in the future, so that it can be logically expanded with a combination of truth, truth, facts, definitions, rules and common sense, and the trust that performed the evaluation. It shall be identified and recorded as highly reliable information.
In this way, information is converted into patterns, information types and characteristics are identified, classified and recorded, and information-information relationships (logical relationships, similar relationships, reciprocal relationships, analogies, etc.) Information can be constructed as a knowledge system by expressing the connection relationship between patterns.

FIG. 5 shows how input information is evaluated. In the first stage, information reliability is evaluated. This can be done by checking the reliability of the information source (when, who, where from?).
In the second stage, an information field / theme analysis is performed. It is possible to detect the field / theme from the words included in the information.
In the third stage, information type identification is performed. Among the types of input information, truth, truth, facts, definitions, rules, and common sense shall be specified by humans. Explanations, hypotheses, predictions, opinions, impressions, etc. can be identified from the words (I think, think, think ...) included in the information. Identification of ordinary sentences, question sentences, command sentences, conditional sentences, and exclamation sentences can also be performed from included words.
In the fourth stage, the interest level is evaluated. It is possible to evaluate by setting a field / theme of the information of interest in advance and checking whether or not it matches with the field / theme analysis result of the information.
In the fifth stage, novelty evaluation is conducted. This irradiates the recording area with input information and a pattern that is semantically equivalent to the input information, and confirms the presence or absence of a related pattern. When a related pattern is detected, it can be implemented by comparing the differences between the patterns and evaluating whether or not there is a new pattern.
In the sixth stage, validity evaluation is performed. This irradiates the recording area where the relation between words is defined with the pattern of the input information and the related information, and evaluates the matching and mismatching for each sentence element. If inconsistencies are detected, the reliability of input information and related information (truth, truth, facts, definitions, rules, common sense, reliability of information sources) is evaluated, and those with high reliability are preferentially adopted. And record it.

6 to 10 show examples of operations for evaluating input information and constructing it as a knowledge system.
FIG. 6 shows an operation example for analyzing information reliability, field, theme and interest level.
In FIG. 7, if the pattern of input information is [PA], the pattern of [PA] is irradiated to the recording area storing the meaning of the word and the meaning of the corresponding word is searched. By performing this search in association with the sentence structure (subject, predicate, subject modification, predicate modification), a pattern that is semantically equivalent to the input information can be generated. This semantically equivalent pattern is expressed as [PA #]. When retrieving related information from input information, a number of retrieval methods can be used. Information that exactly matches the input information, information that is semantically equivalent, information that partially matches, etc. can be selected depending on the purpose of the search.
FIG. 8 shows that a semantically equivalent pattern [PA #] is emitted and [PB] is detected as related information. Similarly, semantic analysis is performed on [PB] to generate a semantically equivalent pattern [PB #].
FIG. 9 shows the analysis of the relationship between the input information and the related information already recorded. A matching pattern or a different pattern can be extracted from the difference between [PA #] and [PB #]. Further, it is possible to detect a semantic difference by irradiating a recording area in which the relationship between words is defined (equivalent, similar, opposite, etc.) with [PA #] and [PB #].
In the recording area where the relationship between the words is defined, the relationship between the words is identified by the connection relationship. For this reason, if the word excited by the input information and the word excited by the related information are semantically the same, the pattern indicating the same meaning is excited and can be identified as having the same meaning. On the other hand, if the word whose input information is excited and the word whose related information is excited are semantically opposite, the pattern indicating the opposite meaning is excited, and it can be identified that the meaning is opposite. FIG. 10 shows the operation of evaluating the usefulness of the input information, and adding the identification result such as the type of information, the field, the theme, and the degree of interest as incidental information and recording it in a predetermined recording area when it is evaluated as useful Is shown.

If information is constructed as a knowledge system in this way, problem solving using the knowledge system becomes possible. First, the problem is analyzed using the knowledge system, the problem is clarified, and the goal of problem solving is set. Next, a solution plan for solving the problem is extracted and applied. The situation after application is evaluated, and if the goal of problem solving is reached, the process is completed. If the problem solving goal has not been reached, the above process is applied again to the new state to bring it closer to the problem solving goal.
The problem analysis and problem clarification are also implemented by learning how to detect and identify the difference between the target state and the current situation. Moreover, the solution proposal corresponding to each problem is implemented by learning the corresponding processing. Learning is performed by recording a history of pattern transitions. The content taught by humans is recorded as a transition from pattern to pattern.
As learning progresses, the taught process is autonomously executed as a transition from pattern to pattern, and the taught content is executed.

The transition of human thought patterns can be generally expressed as a conditional process from the viewpoint that the transition destination changes depending on the condition. In the present invention, the meaning of the language is interpreted and autonomously converted into conditional processing as necessary. The condition of the conditional processing is determined by generating a search pattern from the corresponding language and searching autonomously. Judgment is made as to whether or not the retrieved information satisfies the condition, and if satisfied, the corresponding processing is executed. Even if a problem solution that humans use as knowledge is input as a language as it is, the meaning is sequentially interpreted and conditional processing proceeds autonomously to solve the problem. When new information is required during the process, the information request is notified, and when the corresponding information is acquired, the process according to the content is performed.
In the learning phase, when a pattern is excited, the history of the pattern excited in the set period before the pattern is excited is referred to, and the connection relationship with the excited pattern is strengthened. In the practical phase, a part of the recorded excitation history is used to check whether the excitation condition recorded in each recording unit is met, and the recording unit that matches the excitation condition is excited. When the pattern is excited, the history of the excitation pattern is updated, the above processing is performed in a new state, and it is confirmed whether the excitation conditions recorded in each recording unit are met. Excited and autonomously search for information about processing.

In this way, in the learning phase, a series of patterns corresponding to processing is sequentially excited by a human instruction, and by recording the history, the corresponding pattern is excited according to the instructed procedure. The behavior of the pattern is not static but shows dynamic behavior. Using the internal pattern recorded in the pattern, it is possible to search for necessary information and store the search result in a required place. Also, it is possible to perform processing such as converting the internal pattern recorded in the pattern into a designated arrangement. Numerous processes (formula processing, chemical formula processing, translation, etc.) can be performed by combining patterns having such dynamic behavior.
Teaching to this machine can be performed by inputting natural language sequentially without programming. The input language information is analyzed for syntax, meaning, and relationship with already recorded information, and the corresponding pattern is excited according to the analysis result, and processing is executed. Numerous processes such as value evaluation and recording of input information, execution of instructed instructions, and generation of solutions to problems / issues are possible.
The overall operation of this machine is managed by the pattern controller. In each pattern transition cycle, information input, information analysis (sentence type, syntax, meaning, etc.), information evaluation (novelty, reliability, validity, usefulness, etc.) and information processing (problem / problem solution) Generation, recording, information output, etc.).

FIG. 11 shows an execution example of the instruction content. It shows the operation of identifying a conditional process from an input sentence (language) and executing the process autonomously while confirming the establishment of the condition. By analyzing the input sentence, an in-sentence pattern corresponding to the condition is extracted from the pattern corresponding to the input sentence, and a search pattern [PQ] for the condition search is generated. Patterns corresponding to the conditions in the search pattern by irradiating the pattern recorder with the search pattern [PQ]
Record the information. If no information on the condition is recorded in the pattern recorder, the fact is notified and an additional input of information is requested.
When information corresponding to the condition is retrieved, the information is collated with the condition described in the input sentence. Processing is determined depending on whether or not the collation results match. If the collation matches, the pattern corresponding to the process described in the input sentence is excited and the process is executed.

  FIG. 12 shows an operation of analyzing an input language (question) and generating an answer to the question autonomously. When it is detected that the input sentence is a question, a search pattern corresponding to the question is generated from the in-sentence pattern included in the input sentence. Since the input sentence has already been analyzed for the subject, predicate, subject modification, predicate modification (what, when, where, why, how), the search pattern asks what information in the sentence element Can be identified. A search pattern is generated from the identification result. Next, the search pattern is irradiated to the pattern recorder, and related information is searched. Of the patterns that show correlation by irradiation of the search pattern, the pattern that shows the highest correlation with the search pattern and has information at the location of the question is stored as the search result. If necessary, add a process to check whether the search result satisfies the expected answer condition. A pattern corresponding to the answer to the question is generated and output from the search result and the search pattern. The pattern is converted into information (language) by a pattern inverse converter and output.

FIG. 13 shows an operation in which the history of the excited pattern is recorded in the pattern irradiator. When a certain recording unit is excited, it generates connection relation data from the history data of the pattern excited before that and records it in the connection relation recording section of the recording unit of the pattern. It shows the operation that strengthens the connection relationship. The pattern irradiator collates the excitation pattern history data set from the present time to the past with the connection relation data recorded in the connection relation recording section of each recording unit, and excites the recording unit having a large correlation.
FIG. 14 shows an operation in which the connection relationship between the corresponding patterns is strengthened when the excitation pattern repeatedly appears.
Next, generalization of input information will be described.
Feature extraction and generalization of a part of a word included in an input sentence For example, proper nouns appearing in sentences are generalized as person A, person B, object C, object D. Sequentially, sentences are converted into patterns, and the converted patterns are excited. At this time, the generalized pattern is also sequentially excited. By inputting a lot of information, the connection between specific patterns constituting a sentence is strengthened. The input sentence has a relationship with a nearby sentence. Since this relationship is expressed by the relationship between words constituting a sentence, the same relationship is often expressed by a combination between the same words or similar words. Each time the pattern of the sentence is excited, the words in the sentence are excited. By generalizing proper nouns, etc., the relationship between the generalized person and person or thing will be emphasized. Words and sentences appear more frequently than when they are expressed with proper nouns.
With this effect, the relationship between objects that does not depend on proper nouns is extracted. Further, since the feature extraction of words extracts not the relationship between individual words but the relationship between word features, generalization from the feature side becomes possible. This relationship can be detected because the connection relationship between specific patterns is strengthened. In particular, relationships strengthened from many sentence examples have generality, and those corresponding to common sense or general ideas are extracted. This common sense or general idea depends on the text group to be entered. That is, when a sentence group corresponding to a certain culture is input, common sense or a general idea corresponding to the culture is extracted. When a group of sentences in the same culture is input, the way of thinking is considered to be equivalent. Therefore, the way of thinking strengthened by many people in the same culture is extracted.
FIG. 15 shows a common sense by generating a pattern in which some words of an input sentence are generalized or feature extracted, and by sequentially inputting the generalized pattern and strengthening the connection relation between the patterns corresponding to the generalized words. In addition, an operation example for constructing a general idea is shown.
Similarly, by extracting and generalizing the characteristics of some words for a number of problems and their solutions and operating them, a generalized relationship between the problems and their solutions can be extracted. As learning by information input progresses, this machine can autonomously generate a solution to a similar problem.
FIG. 16 shows an operation example in which a problem and a solution pattern are generalized and input, and a problem and a solution are generalized by strengthening a connection relation between the patterns.

FIG. 17 to FIG. 20 show an operation example in which processing is executed directly from an input language without programming.
FIG. 17 shows an operation example in which an input sentence is irradiated onto a recording area for detecting a sentence type, the sentence type (normal sentence, question sentence, command sentence, conditional sentence) is identified, and a processing sequence corresponding to the sentence type is called. Shows about. FIG. 18 shows that when an input sentence (language) corresponding to conditional processing is input, information corresponding to the condition is autonomously searched by exciting a pattern for confirming the condition, and the searched information is input sentence. When matching with the conditions described in (1), an operation for executing the corresponding process if it matches is shown.
FIG. 19 shows an operation example in which knowledge recorded in a language is used to solve a problem autonomously. A pattern indicating the target state is called to detect a difference from the target state. Exciting patterns for analyzing causes and problems and corresponding countermeasure patterns from patterns showing differences. When the pattern corresponding to the countermeasure is executed and the state changes, the above process is repeated until the target state is reached for the changed state.
FIG. 20 shows an operation example in which information is recorded in a pattern recorder in a state where information is identified by a condition part and a processing part and organized as a sentence structure. By analyzing the information word, the condition part and the processing part of the information can be identified. For example, in the statements “A”, “GA”, “B”, “NO”, “C”, “do it”, [“A”, “GA”, “B”, “NO” ”is the condition part, and [“ C "Perform""is a processing unit. Also, in the sentence “A” “ga” “B” “if” “C” “ha” “D” “is” [“A” “ga” “B” “if”] is the condition part, [“C” “is” “D” “is”] is a processing unit.
In this way, the sentence is divided into a conditional part and a processing part, and converted into a structure having inter-pattern connection for conditional processing. Further, when the pattern of the condition part is excited, a process for autonomously confirming whether or not the condition part is established is excited. When a sentence is converted into a pattern with a conditional processing structure in this way, an active thinking action is realized in which sentences that can be expressed by conditional processing proceed sequentially while confirming the validity of the condition. be able to. In general, human problem solving and action determination can be expressed by conditional processing. By inputting human problem-solving solutions and action-determining measures in a language (sentence), the machine autonomously converts it into conditional processing, and then proceeds with the processing while confirming the validity of the conditions. It becomes possible. By inputting knowledge (procedures and thinking methods related to problem solving and action determination) expressed in a language (sentence) without programming actions corresponding to human problem solving and action determination, human beings can solve problems by thinking or It becomes possible to carry out problem solving or action determination so as to make action decisions.

26 to 59 show an example of the operation of the program produced according to the present invention. In this program, a part corresponding to various processes in information processing is set in a natural language, and a part related to a function for selecting a way of thinking and a relation between information is produced by the program. The functions of the recording unit and pattern irradiator were realized by using an array of programming languages. (Recording unit connection relationship recording unit uses a three-dimensional array, and records the connection relationship with the connection destination in each step for each unit. Patterns in each unit, connection relationships between patterns, and patterns with connection relationships The function is realized by using a two-dimensional array, and the excitation history recording in the pattern irradiator is also used cyclically (to avoid an increase in recording capacity). It is realized by.)
When a word string is input, the recording units corresponding to the corresponding word are sequentially excited. The connection relation recording unit of the recording unit corresponding to the word records a pattern corresponding to the word string, and when the corresponding word string is irradiated from the pattern irradiator, the corresponding word recording unit is excited. . When the recording unit corresponding to the word is excited, the meaning of the word and the recording unit corresponding to the part of speech are also sequentially excited. That is, when the recording unit corresponding to the word string is sequentially excited, the recording unit corresponding to the word, the meaning of the word, and the part of speech of the word is also sequentially excited. When the recording unit corresponding to the word, the meaning of the word, and the part of speech of the word is sequentially excited, a sentence element (subject, subject modification, predicate, predicate modification) is detected and syntax analysis is performed. Sentences newly input by syntactic analysis are recorded in the recording unit in a form that identifies and organizes the subject, subject modification, predicate, and predicate modification.
26 to 28 show an operation in which an example sentence is input, a word, a meaning of the word, and a part of speech are identified from a word string, and a sentence element (subject, predicate, modifier) is identified. Since syntax analysis is performed and sentence elements are identified, translation is facilitated, so information on recording units corresponding to English words corresponding to each word or sentence element is also shown. English sentence elements corresponding to Japanese sentence elements can be generated simultaneously. Since the relationship between the main part, predicate, subject, subject modification, predicate, predicate modification, and modification is clarified by the syntax analysis, sentence elements converted to English words are converted into appropriate arrays according to English English grammar. Basic translation can be performed by converting. In Japanese, the predicate is generally located at the end, and the modification is often applied to the next sentence element sequentially from the front. In English, a predicate is positioned after the subject, followed by a modifier that modifies the predicate, followed by a description of the modifier, and the direction of modification is in the reverse order of Japanese. In machine translation, it is difficult to identify which word modifies which word, but in the present invention, the modification relationship between these sentence elements is identified during processing, so conversion to English word order is not possible. Easy. In FIG. 23, this conversion performs the pattern conversion from the E1 pattern to the final form E2 by applying the A2 pattern. FIG. 22 shows the transition between these patterns using a configuration diagram. Examples of translations in which sentences and answers to questions input by syntactic analysis and English word conversion are converted into English are shown in FIGS.
FIG. 24 shows an example of a functional configuration of a method for detecting and autonomously solving a problem. The problem is solved step by step by detecting the problem from the state and applying a solution corresponding to the detected problem. FIG. 25 shows an example of pattern transition in this operation.
Some problems involve summarizing or refining a given sentence. For such a problem, information can be compressed or expanded using the relationship formed between the information. Since relationships (causes and results, events and reasons, explanations and conclusions, outlines and details, similar contents, and opposition opinions, etc.) are set, information can be obtained by appropriately setting these relationships. It can be compressed or expanded.
Next, an operation example of the artificial intelligence apparatus will be described by inputting various sentence examples.
The example sentences entered are shown below. (Enter the word “Hiragana” to this machine)
Figure 26 Example sentence 1: Mise a's recommendation is Wain.
= (Recommendation of store A is wine)
Figure 26 Example sentence 2: Mise b's recommendation is waiin.
= (Recommendation of store B is wine)
Figure 26 Example sentence 3: The recommendation of Mise c is Nihonshu.
= (Recommendation of store C is sake.)
Figure 26 Example sentence 4: Mise d recommends it.
= (Recommendation of store D is wine)
Figure 27 Example sentence 5: Misese recommends Nihonshu.
= (Recommendation of store E is sake.)
Fig. 27 Example sentence 6: Mise a has a distance of 0.5 km.
= (Distance of store A is 0.5km.)
Figure 27 Example sentence 7: The length of Mise b is 1.2 km.
= (Distance of store B is 1.2km.)
Fig. 28 Example sentence 8: The height of Mise c is 0.8 km.
= (Distance of store C is 0.8km.)
Fig. 28 Example sentence 9: The distance of Mise d is 1.5 km.
= (Distance of store D is 1.5km.)
Figure 28 Example sentence 10: The distance of Mise is 0.3 km.
= (Distance of store E is 0.3km.)
FIG. 29 shows an operation example in which, when information is input, comparison is made for each sentence element with information already recorded, and novelty is analyzed. The meanings of the symbols in the figure are shown below.
Cmpsts1 line: collation result between sentence elements corresponding to the subject Cmpstol1 line: collation result between sentence elements corresponding to the object (what) Cpsth1 line: collation between sentence elements corresponding to modifiers (when) Result cmpsth2 line: Matching result between sentence elements corresponding to modifier (where) Cmpsth3 line: Matching result cmpsth4 between sentence elements corresponding to modifier (why): Modifier (how) Matching result cmpstvt line between corresponding statement elements: Matching result cmpstvv between statement elements corresponding to a predicate (how): Matching result cmpstvq line between statement elements corresponding to a predicate (how): Predicate Comparison result total line between statement elements corresponding to (how did it): Specify as a search condition among the sentence elements included in the sentence to be searched. How to sentence elements and the collation result of the product (AND condition)
recnomax: identification number assigned to the recording area of the sentence FIG. 30 converts the input information into a pattern (in this example, assigning a word identification number (number) as a pattern corresponding to each word, part of speech, and assigning a part of speech identification number (number)) The meanings of the symbols in the figure are as follows:
Line m1: Word number of the word that modifies the subject (1)
Line m2: Word number of the word that modifies the subject (2)
s1 line: the word number of the subject (1)
s2 line: subject word number (2)
o1 line: word number of object (what) (1)
o2 line: word number of object (what) (2)
h11 line: word number of modifier (what time) (1)
Line h12: Word number of modifier (what time) (2)
h21 line: word number of modifier (where) (1)
Line h22: Word number of the modifier (where) (2)
h31 line: word number of modifier (why) (1)
Line h32: Word number of the modifier (why) (2)
h41 line: word number of modifier (how) (1)
h42 line: word number of modifier (how) (2)
Line v1: Predicate word number (1)
v2 line: word number of predicate (2)
FIG. 31 to FIG. 34 show an operation example in which a number of questions relating to inputted information can be answered by inputting information to the artificial intelligence apparatus.
I1 to I10 in FIG. 31 indicate that the previous example sentence is input and the interpretation of meaning (identification of words from the input word string, syntax analysis) is correctly performed and conversion into English is also valid. Since information is recorded in the artificial intelligence apparatus by the example sentence, it is assumed that a number of questions shown in Q1 to Q20 are input next.
Q1 to Q5 are questions for searching “what” and “What” as questions. A search key for extracting an answer from each question sentence is autonomously generated, a word corresponding to the answer is extracted, and the answer sentence is correctly generated.
Q6 to Q11 are operation examples for the question sentences answered with “Yes”, “Yes”, “No”, and “No”. The questioned content is recognized as correct and collated with recorded information to identify whether the content of the question is correct and generate an answer. Answers are correctly generated for all questions.
Q12 to Q18 are questions including numerical values. The numerical value of the input information is converted from character information to numerical information and recorded. A correct answer sentence is generated by comparing the numerical value corresponding to the question with the recorded numerical value.
Q19 and Q20 are questions for extracting optimum information from the recorded information. The information that best matches the conditions set in the question is extracted and the correct answer sentence is generated.
35 and 36 show the process of generating answers to Q19 and Q20 questions.
No., “Distance”, “First”, “Short” or “Long” are extracted from the question sentence and a processing program for generating an answer is autonomously started. In the example of FIG. 35, the “distance” is “most” and “shortest”.
Search the “separated” information, identify the recording unit corresponding to the “first” and “shortest” from the retrieved distance information, and generate the “distance” of the corresponding answer “store E” from the identified recording unit Yes. Similarly, in FIG. 36, the problem is that the “distance” is “first” or “longest”, and a correct answer is generated.
37 to 59 show an example in which a number of processing programs are started from input information, new information is generated and incorporated into the knowledge system, and further problems are solved using the knowledge accumulated in the knowledge system. It is a thing.
FIGS. 37 to 50 show an operation example in which the area and volume of a figure are autonomously solved from information on the figure shape and feature amount.
In FIG. 37, the following information is input.
(1) The shape of figure A is a circle.
(2) The radius of figure A is a.
(3) a is 1.
In FIG. 38, after the above information is input, the next question is input.
(4) What is the area of figure A?
At this time, the artificial intelligence apparatus performs the following operation to obtain the area.
・ Search whether there is information corresponding to the answer of the problem in the recording area ・ Processing program to solve the problem when there is no information corresponding to the answer of the problem (Launch the area calculation program)
-Shape search-> detect that the shape is (circle)-Search for radius information of (circle)-> detect that the radius is 1-Derive the area of (circle) from the radius information Figure 39 is input to the recording unit The operation | movement which the sentence is recorded sequentially is shown. Radius information (1) is input at sentence number 3 (a part of several in), the area of the circle is derived, and the result is output at the lower part of sentence number 4. (Location of several outs) Note that the excerpt of the answer text is displayed in the lower part. (Columns rm1-rh42)
In FIG. 40, the following information is input.
(5) The shape of figure B is square.
(6) The radius of figure B is b.
(7) b is 2.
In FIG. 41, after inputting the above information, the next question is input.
(8) What is the area of figure B?
At this time, the artificial intelligence apparatus performs the following operation to obtain the area.
・ Search whether there is information corresponding to the answer of the problem in the recording area ・ Processing program to solve the problem when there is no information corresponding to the answer of the problem (Launch the area calculation program)
-Shape search-> detect that the shape is (square)-Search for (square) side information-> detect that the side is 2-Derive the area of (square) from the side information Fig. 42 and Fig. 43 are recorded An operation in which input sentences are sequentially recorded in the unit is shown. The edge information (2) is input at the sentence number 7 (a part of several in), the area of the circle is derived, and the result is output at the lower part of the sentence number 8. (Location of several outs) Note that the excerpt of the answer text is displayed in the lower part.
(Columns rm1-rh42)
In FIG. 44, the following information is input.
(9) The shape of object C is a sphere.
(10) The radius of object C is c.
(11) c is 1.
In FIG. 45, after the above information is input, the next question is input.
(12) What is the volume of object C?
At this time, the artificial intelligence apparatus performs the following operation to obtain the volume.
・ Search whether there is information corresponding to the answer to the problem in the recording area ・ Processing program to solve the problem when there is no information corresponding to the answer to the problem (Launch the volume calculation program)
-Shape search-> Detect that shape is (sphere)-Search for radius information of (sphere)-> detect that radius is 1-Derived volume of (sphere) from radius information Figures 46 and 47 are recorded An operation in which input sentences are sequentially recorded in the unit is shown. Radius information (1) is input at sentence number 11 (a part of several in), the volume of the sphere is derived, and the result is output at the lower stage of sentence number 12. (Location of several outs) Note that the excerpt of the answer text is displayed in the lower part. (Columns rm1-rh42)
In FIG. 48, the following information is input.
(13) The shape of object D is a cube.
(14) The side of object D is d.
(15) d is 1.
In FIG. 49, after the above information is input, the next question is input.
(16) What is the volume of object D?
At this time, the artificial intelligence apparatus performs the following operation to obtain the volume.
・ Search whether there is information corresponding to the answer to the problem in the recording area ・ Processing program to solve the problem when there is no information corresponding to the answer to the problem (Launch the volume calculation program)
-Shape search-> Detect that the shape is (cube)-Search for (cube) side information-> detect that the side is 2-Derive the volume of (cube) from the side information Figure 50 is input to the recording unit The operation | movement which the sentence is recorded sequentially is shown. The edge information (2) is input at the sentence number 3 (a part of several in), the volume of the sphere is derived, and the result is output to the lower stage of the sentence number 4. (Location of several outs) Note that the excerpt of the answer text is displayed in the lower part. (Columns rm1-rh42)
51 to 59 show an example of an operation for generating an answer to a question as to whether or not an object floats on water.
In FIG. 51, information relating to the weight of the object D (cube) whose volume has been obtained earlier is input. FIG. 52 shows an operation in which additional information is recorded in the recording unit. Information (4) relating to the weight of the object D is input at the sentence number 5. (Number in place)
FIG. 53 shows an operation for deriving the specific gravity of the object D.
This artificial intelligence device performs the following operations to determine the specific gravity.
・ Search whether the information corresponding to the answer to the problem is in the recording area ・ If there is no information corresponding to the answer to the problem, the processing program to solve the problem (Launch the program for determining the specific gravity)
・ Detection of volume and weight → Detect that volume is (8) and weight is (4) ・ Deriving specific gravity (0.5) from volume and weight information In FIG. 6 shows an operation recorded at a location of (several out).
FIG. 55 shows an operation of comparing the specific gravity of the object with the specific gravity of water (1.0) and asking whether or not the specific gravity of the object D is smaller than 1.0. 56. The specific gravity (0.5) of the object D is retrieved from the sentence number 6 (location of several outs) in which the question entered in the sentence number 7 in FIG. 56 is recorded and the specific gravity of the object D is recorded. 1.0) which is a smaller value. Thus, an answer to the question “Is the specific gravity of the object D 1 or less?” Is generated. Sentence number (location from rm1 to rh42)
In FIG. 57, knowledge about a condition that an object floats on water is input. “If the specific gravity of the object D is 1 or less, the object D will appear.” Since this sentence is a conditional sentence, this artificial intelligence device autonomously determines whether the contents of the sentence set as a condition are correct. investigate. Since it is confirmed that “the specific gravity of the object D is 1 or less” as an answer to the question in FIG. 55, it is detected that the condition part is established. Furthermore, a sentence “object D floats” is generated and output as a logical expansion when the condition part is satisfied.
In the above example, some broken questions are set to solve the problem. When human beings solve a problem, they often break down the problem into several small problems and solve the problems that were initially set while solving each small problem. This artificial intelligence device can also break down problems into smaller problems and solve large problems while solving individual problems. Although the method of extracting a problem and solving it autonomously in FIGS. 24 and 25 has been described, it can be realized by this method. So-called problem solving methods and solving methods are prepared as a library for a number of problems, and each problem is detected and a processing program for problem solving is autonomously executed. It is possible to solve difficult problems. The knowledge that solves a wide variety of problems is searched using the relationship, and the processing program is activated by the searched knowledge to acquire the knowledge. Incorporate newly acquired knowledge into the knowledge system, extract the issues to solve the target problem in that state, and apply the solutions to solve the problem initially set .
In this embodiment, an example of a simple function as a processing program has been described. However, it is possible to greatly expand the capability of the artificial intelligence apparatus by adding a high-functionality processing program. For example, an equation group such as physics, chemistry, mathematics, etc. is incorporated into a processing program, and an equation solution to be used can be obtained by setting an equation to be used, an initial value, and a precondition. The result can be predicted by so-called simulation. It is possible to solve more advanced and complicated problems by conducting case studies by several types of simulations, etc., and selecting the means with the highest evaluation.
Source of information, reliability, novelty, field, subject, subject, subject modification, predicate, predicate modification, modification relationship, when, where, who, what, how, why We analyzed the relationship with the recorded related information, organized it into a sentence structure, recorded it, autonomously recorded the information judged useful, built it as a knowledge system and built the input information inside Evaluation can be performed by collating with the knowledge system, and processing (recording of information, updating / improvement of the knowledge system, execution of instruction contents, answer to questions) according to the evaluation result can be autonomously performed. Further, in solving problems, the constructed knowledge system can be utilized, and a processing program necessary for problem solving can be activated autonomously, and processing results can be incorporated into the knowledge system to improve the knowledge system.

Effect of the invention

According to the present invention, information source, reliability, novelty, field, theme, subject, subject modification, predicate, predicate modification, modification relationship, when, where, who, what, how In addition, it is possible to analyze why it was done, organize it into a sentence structure, and autonomously record information that is judged useful, and construct it as a knowledge system. In addition, the input information is evaluated against the knowledge system built internally, and processing (recording of information, updating / improvement of the knowledge system, execution of instructions, answering questions) is performed autonomously according to the evaluation results. To do. In addition, some of the words included in the information are generalized, and the relationship between the information that is input sequentially and the relationship between the information is enhanced by strengthening the relationship between the set of patterns corresponding to the information and the set of patterns. It is possible to extract relationships and autonomously construct common sense, general ideas and problem solving methods from a series of input information.
Conventionally, when a machine performs processing of input information, etc., it has been necessary to set a program in the computer in advance. It was necessary to create a program for judging the situation from the input information and a program for causing the machine to operate according to individual conditions, and to implement and execute it on a computer mounted on the machine. The program had to be created in a dedicated programming language, and there was a demerit that it took a lot of time for development. If the detection of the condition and the corresponding operation are not appropriate, there is a demerit that it is necessary for a person to correct the program installed in the computer, and it takes a long time for the correction.
According to the present invention, processing is performed by learning how to process information, so that it is not necessary to perform sequential programming. By inputting information on the processing method in a natural language, the machine records and learns the processing method, so that labor can be greatly reduced.
In addition, since the process can be changed by changing the pattern and changing the connection between the patterns regardless of the program, the system is very flexible and adaptable.
In addition, regarding a problem that can be solved by human thought (language), the artificial intelligence apparatus according to the present invention follows the input problem solving method or procedure by indicating the method or procedure for solving the problem in natural language. Can solve the problem autonomously.
If there is an ambiguity or uncertain point in the problem solving technique or procedure, the problem can be solved by notifying each time and clarifying the solution technique and procedure. Furthermore, by inputting human problem solving measures and action decision measures in natural language (sentences), the present invention autonomously converts them into conditional processing, and then proceeds with the processing while confirming the establishment of the conditions. Is possible. By inputting knowledge (procedures and methods for problem solving and action determination) expressed in natural language (sentences) without programming the actions equivalent to human problem solving and action determination, humans can solve problems through thinking. Alternatively, problem solving or action determination can be performed so as to determine action.
The knowledge system of the present invention is constructed based on truth, facts, rules, common sense, definition and logic.
Information that does not match these is not taken into the knowledge system. All processing steps are transparent systems that can be displayed in human-understood language or symbols. The knowledge system has been constructed using the correct knowledge accumulated by humans as the source, and the system can display the processing status in all processing processes in a form that can be understood by humans. Realization of “artificial intelligence that does not run away” is possible. Further, in solving problems, the constructed knowledge system can be utilized, and a processing program necessary for problem solving can be activated autonomously, and processing results can be incorporated into the knowledge system to improve the knowledge system.

Configuration example of artificial intelligence device Pattern configuration example Example of operation in which words, sentence elements, and modification relationships are identified sequentially from a word string Example of current affairs Input information evaluation example Knowledge system construction example (1) Example of knowledge system construction (part 2) Example of knowledge system construction (part 3) Knowledge system construction example (4) Example of knowledge system construction (part 5) Example of instruction content (Example of conditional processing) Answers to questions Example of operation for strengthening connection between excitation pattern and related pattern (Part 1) Example of operation for strengthening connection between excitation pattern and related pattern (Part 2) Example of building common sense and general ideas Example of how problems and solutions generalize How to execute processing directly from the input language without programming Sequential execution example of input and language group An example of autonomously generating a processing procedure and further generating the next processing procedure for a situation resulting from the processing and advancing the processing Example of operation that records the entered information as a sentence structure by identifying it in the condition part and the processing part Overview of recording unit functions Functional configuration example of English translation Example of pattern transition to English translation Functional configuration example of the method of autonomously generating answers by interpreting the meaning of the problem Pattern transition example of a method of autonomously generating answers by interpreting the meaning of problems Program execution example (part 1) Example of syntax analysis (example sentence 1 to example sentence 4) Program execution example (part 2) Example of syntax analysis (example sentence 5 to example sentence 7) Program execution example (part 3) Example of syntax analysis (example sentence 8 to example sentence 10) Example of program execution (part 4) Example of syntax analysis (detection of input sentence novelty) Example of program execution (part 5) Example of syntax analysis (input sentence recording example) Program execution example (6) Example of syntax analysis (input information and answers to questions 1) Example of program execution (part 7) Example of syntax analysis (input information and answers to questions 2) Example of program execution (8) Example of syntax analysis (input information and answer to question 3) Program execution example (part 9) Example of syntax analysis (input information and answers to questions 4) Program execution example (10) Example of syntax analysis (input information and answers to questions 5) Program execution example (part 11) Example of syntax analysis (input information and answer to question 6) Program execution example (part 12) Problem solving example (circle area 1) Program execution example (part 13) Problem solving example (circle area 2) Program execution example (part 14) Problem solving example (circle area 3) Program execution example (15) Problem solving example (square area 1) Program execution example (16) Problem solving example (square area 2) Program execution example (part 17) Problem solving example (square area 3) Program execution example (18) Problem solving example (square area 4) Program execution example (19) Problem solving example (sphere volume 1) Program execution example (part 20) Problem solving example (sphere volume 2) Program execution example (part 21) Problem solving example (sphere volume 3) Program execution example (part 22) Problem solving example (sphere volume 4) Program execution example (23) Problem solving example (cube volume 1) Program execution example (24) Problem solving example (cube volume 2) Program execution example (part 25) Problem solving example (cube volume 3) Program execution example (26) Problem solving example (cube volume 4) Program execution example (27) Problem solving example (buoyancy problem 1) Program execution example (28) Problem solving example (buoyancy problem 2) Program execution example (29) Problem solving example (buoyancy problem 3) Program execution example (part 30) Problem solving example (buoyancy problem 4) Program execution example (31) Problem solving example (buoyancy problem 5) Program execution example (32) Problem solving example (buoyancy problem 6) Program execution example (33) Problem solving example (buoyancy problem 7) Program execution example (part 34) Problem solving example (buoyancy problem 8)

1 Pattern Converter 2 Pattern Recorder 3 Pattern Analyzer 4 Pattern Controller 5 Pattern Reverse Converter 6 Pattern Irradiator

Claims (16)

It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder Artificial intelligence device that constructs autonomously. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, Equipped with a pattern analyzer that analyzes the relationship between symbols and related information recorded and organizes the information into an easily searchable structure and records it in a pattern recorder, analyzes sentences entered by human instructions and learning, An artificial intelligence device that autonomously generates search conditions and searches for objects that satisfy the conditions. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder An artificial intelligence device that interprets the meaning of a problem using the built knowledge system and autonomously generates answers to the problem. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder Artificial intelligence device that interprets the meaning of the problem using the built knowledge system, autonomously generates answers to the problem, and incorporates the generated answers into the knowledge system to further improve knowledge. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder And the knowledge system constructed to interpret the meaning of the problem using the constructed knowledge system, information retrieval to solve the problem, generation of information necessary for problem solving and processing, and knowledge system of the processing result Autonomous implementation Artificial intelligence device, and performs further improvement of knowledge. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder And the knowledge system built, interpret the meaning of the problem, expand the problem to lower problems, search for information to solve the problem, start the program necessary for problem solving, and knowledge of the processing results Incorporation into the system To implement, artificial intelligence device which performs further improve the knowledge. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder An artificial intelligence device that autonomously constructs and solves a given problem using the relationship between the constructed knowledge system and information. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder An artificial intelligence device that autonomously constructs and summarizes or refines a given sentence using the relationship between the constructed knowledge system and information. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder Software to function as an artificial intelligence device that autonomously constructs It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, Equipped with a pattern analyzer that analyzes the relationship between symbols and related information recorded and organizes the information into an easily searchable structure and records it in a pattern recorder, analyzes sentences entered by human instructions and learning, Software that functions as an artificial intelligence device that autonomously generates search conditions and searches for objects that satisfy the conditions. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder Is a software that allows you to function as an artificial intelligence device that interprets the meaning of a problem using the built knowledge system and generates answers to the problem autonomously. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder And using the constructed knowledge system to interpret the meaning of the problem, autonomously generate answers to the problem, and incorporate the generated answers into the knowledge system to function as an artificial intelligence device that further improves knowledge Software for. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder And the knowledge system constructed to interpret the meaning of the problem using the constructed knowledge system, information retrieval to solve the problem, generation of information necessary for problem solving and processing, and knowledge system of the processing result Autonomous implementation Software to function as an artificial intelligence device, and performs further improvement of knowledge. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder And the knowledge system built, interpret the meaning of the problem, expand the problem to lower problems, search for information to solve the problem, start the program necessary for problem solving, and knowledge of the processing results Incorporation into the system Software to function as an artificial intelligence system to implement, performed to further improve knowledge. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder Software to function as an artificial intelligence device that solves a given problem using the relationship between the constructed knowledge system and information autonomously. It consists of a pattern converter that converts information into a pattern, and a recording unit that records verification and matching (excitation) conditions, patterns, connection relationships between patterns, relationships with connected patterns, and pattern analysis results. The pattern recorder and the relationship between the matching and matching (excitation) conditions, patterns, connection relationships between patterns and patterns with connection relationships are registered or changed by human instructions or autonomously, and the state and analysis of patterns Record the pattern excitation history according to the results and the pattern excitation history, and use the recorded excitation history information to check whether the matching (excitation) conditions recorded in each recording unit are satisfied. A pattern irradiator that excites the recording unit that satisfies the matching (excitation) conditions, a pattern inverse converter that converts the pattern into information, and the input information Information sources, credibility, novelty, subject, subject, subject, subject modification, predicate, predicate modification, modifier relationship, when, where, who, what, how, why Or sentence type (normal sentence, question sentence, imperative sentence, conditional sentence, truth, fact, rule, common sense, definition, logic, explanation, hypothesis, prediction, opinion, impression, rumors), formula, physical formula, chemical formula, A knowledge system that has a relationship between information and information, with a pattern analyzer that analyzes the relationship between symbols and related information recorded, organizes the information into a structure that is easy to search, and records it in a pattern recorder Software to function as an artificial intelligence device that autonomously constructs and summarizes or refines a given sentence using the relationship between the constructed knowledge system and information.