Authors:
Andrzej Bieszczad
and
Skyler Kuchar
Affiliation:
California State University Channel Islands, United States
Keyword(s):
Neural Network, Neurosolver, General Problem Solving, Search, State Spaces, Temporal Learning, Neural Modeling.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Higher Level Artificial Neural Network Based Intelligent Systems
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Network Hardware Implementation and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Self-Organization and Emergence
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
Neurosolver is a neuromorphic planner and a general problem solving (GPS) system. To acquire its problem
solving capability, Neurosolver uses a structure similar to the columnar organization of the cortex of the brain
and a notion of place cells. The fundamental idea behind Neurosolver is to model world using a state space
paradigm, and then use the model to solve problems presented as a pair of two states of the world: the current
state and the desired (i.e., goal) state. Alternatively, the current state may be known (e.g., through the use of
sensors), so the problem is fully expressed by stating just the goal state. Mechanically, Neurosolver works as
a memory recollection system in which training samples are given as sequences of states of the subject system.
Neurosolver generates a collection of interconnected nodes (inspired by cortical columns), each of which
represents a single point in the problem state space, with the connections representing state transitions. A
connection m
ap between states is generated during training, and using this learned memory information,
Neurosolver is able to construct a path from its current state, to the goal state for each such pair for which a
transitions is possible at all. In this paper we show that Neurosolver is capable of acquiring from scratch the
complete knowledge necessary to solve any puzzle for a given Towers of Hanoi configuration.
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