From the Publisher:
Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the research in ILP has in fact come from machine learning, particularly in the evolution of inductive reasoning from pattern recognition, through initial approaches to symbolic machine learning, to recent techniques for learning relational concepts. In this book they provide an extended, up-to-date survey of ILP, emphasizing methods and systems suitable for software engineering applications, including inductive program development, testing, and maintenance.
Inductive Logic Programming includes a definition of the basic ILP problem and its variations (incremental, with queries, for multiple predicates and predicate invention capabilities), a description of bottom-up operators and techniques (such as least general generalization, inverse resolution, and inverse implication), an analysis of top-down methods (mainly MIS and FOIL-like systems), and a survey of methods and languages for specifying inductive bias.
Logic Programming series
Cited By
- Muggleton S, Schmid U, Zeller C, Tamaddoni-Nezhad A and Besold T (2018). Ultra-Strong Machine Learning, Machine Language, 107:7, (1119-1140), Online publication date: 1-Jul-2018.
- Niskanen A, Wallner J and Järvisalo M Synthesizing argumentation frameworks from examples Proceedings of the Twenty-second European Conference on Artificial Intelligence, (551-559)
- Sankaranarayanan S, Ivančić F and Gupta A Mining library specifications using inductive logic programming Proceedings of the 30th international conference on Software engineering, (131-140)
- Coghill G, Srinivasan A and King R (2008). Qualitative system identification from imperfect data, Journal of Artificial Intelligence Research, 32:1, (825-877), Online publication date: 1-May-2008.
- Kersting K An Inductive Logic Programming Approach to Statistical Relational Learning Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning, (1-228)
- Bongard J and Lipson H (2018). Active Coevolutionary Learning of Deterministic Finite Automata, The Journal of Machine Learning Research, 6, (1651-1678), Online publication date: 1-Dec-2005.
- De Raedt L, Kersting K and Torge S Towards learning stochastic logic programs from proof-banks Proceedings of the 20th national conference on Artificial intelligence - Volume 2, (752-757)
- De Raedt L and Kersting K (2003). Probabilistic logic learning, ACM SIGKDD Explorations Newsletter, 5:1, (31-48), Online publication date: 1-Jul-2003.
- Hernández-Orallo J and José Ramírez-Quintana M (2019). Predictive Software, Automated Software Engineering, 8:2, (139-166), Online publication date: 1-Apr-2001.
- McCluskey T and West M (2019). The Automated Refinement of a Requirements Domain Theory, Automated Software Engineering, 8:2, (195-218), Online publication date: 1-Apr-2001.
- Kijsirikul B, Sinthupinyo S and Chongkasemwongse K (2019). Approximate Match of Rules Using Backpropagation Neural Networks, Machine Language, 44:3, (273-299), Online publication date: 1-Sep-2001.
- An introduction to inductive logic programming Relational Data Mining, (48-71)
- Lavrač N and Flach P (2001). An extended transformation approach to inductive logic programming, ACM Transactions on Computational Logic (TOCL), 2:4, (458-494), Online publication date: 1-Oct-2001.
- Rodríguez J, González C and Boström H Learning First Order Logic Time Series Classifiers Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, (299-308)
- Fürnkranz J (1999). Separate-and-Conquer Rule Learning, Artificial Intelligence Review, 13:1, (3-54), Online publication date: 1-Feb-1999.
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