A new algorithm to automate inductive learning of default theories
| Autor: | Elmer Salazar, Gopal Gupta, Farhad Shakerin |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Logic in Computer Science Computer science Default logic Commonsense reasoning 0102 computer and information sciences 02 engineering and technology 01 natural sciences Logic in Computer Science (cs.LO) Theoretical Computer Science Default reasoning Computational Theory and Mathematics Inductive logic programming 010201 computation theory & mathematics Artificial Intelligence Hardware and Architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Noisy data Algorithm Software |
| Zdroj: | Theory and Practice of Logic Programming. 17:1010-1026 |
| ISSN: | 1475-3081 1471-0684 |
| DOI: | 10.1017/s1471068417000333 |
| Popis: | In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories. Default logic is what humans employ in common-sense reasoning. Therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs. Experiments reported in this paper show that our algorithms are a significant improvement over traditional approaches based on inductive logic programming. Comment: Paper presented at the 33rd International Conference on Logic Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1, 2017 16 pages, LaTeX, 3 PDF figures (arXiv:YYMM.NNNNN) |
| Databáze: | OpenAIRE |
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