Learning first-order acyclic horn programs from entailment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1446, Issue , 1998, Pages 23-37

  Reddy, Chandra ^a   Tadepalli, Prasad ^a  

^a Oregon State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER CIRCUITS; POLYNOMIAL APPROXIMATION;

FIRST ORDER; MEMBERSHIP QUERY; POLYNOMIAL-TIME;

INDUCTIVE LOGIC PROGRAMMING (ILP);

EID: 84947440776     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0027308     Document Type: Conference Paper

References (18)

1. Angluin, D. (1988). Queries and concept learning. Machine Learning, 2, 319-342.
2. Arimura, H. (1997). Learning acyclic first-order horn sentences from entailment. In Proceedings of the Eigth International Workshop on Algorithmic Learning Theory. Ohmsha/Springer-Verlag.
3. Cohen, W. (1995a). Pac-learning non-recursive prolog clauses. Artificial Intelligence, 79(1), 1-38.
4. Cohen, W. (1995b). Pac-learning recursive logic programs: efficient algorithms. J. of AI Research, 2, 500-539.
5. De Raedt, L. (1997). Logical settings for concept learning. Artificial Intelligence, 95(1), 187-201.
6. Džeroski, S., Muggleton, S., & Russell, S. (1992). Pac-learnability of determinate logic programs. In Proceedings of the Fifth Annual A CM Workshop on Computational Learning Theory, pp. 128-135.
7. Erol, K., Hendler, J., & Nan, D. (1994). HTN planning: complexity and expressivity. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94). AAAI Press.
8. Frazier, M., & Pitt, L. (1993). Learning from entailment: An application to propositional Horn sentences. In Proceedings of the Tenth International Conference on Machine Learning, pp. 120-127.
9. Khardon, R. (1996). Learning to take actions. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-g6), pp. 787-792.
10. Khardon, R. (1998). Learning first order universal horn expressions. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory (COLT-g8).
11. Lloyd, J. (1987). Foundations of Logic Programming (2nd ed.). Springer-Verlag, Berlin.
12. Muggleton, S., & Feng, C. (1990). Efficient induction of logic programs. In Proceedings of the First Conference on Algorithmic Learning Theory, pp. 368-381. Ohmsha/Springer-Verlag.
13. Natarajan, B. (1989). On learning from exercises. In Proceedings of the Second Workshop on Computational Learning Theory, pp. 72-87. Morgan Kanfmann.
14. Page, C. D. (1993). Anti-Unification in Constraint Logics: Foundations and Applications to Learnability in First-Order Logic, to Speed-up Learning, and to Deduction. Ph. D. thesis, University of Illinois, Urbana, IL.
15. Plotkin, G. (1970). A note on inductive generalization. In Meltzer, B., & Michie, D. (Eds.), Machine Intelligence, Vol. 5, pp. 153-163. Elsevier North-Holland, New York.
16. Reddy, C., & Tadepalli, P. (1997a). Learning goal-decomposition rules using exercises. In Proceedings of the 11th International Conference on Machine Learning. Morgan Kaufmann.
17. Reddy, C., & Tadepalli, P. (1997b). Learning Horn definitions using equivalence and membership queries. In Proceedings of the 7th International Workshop on Inductive Logic Programming. Springer Verlag.
18. Sammut, C. A., & Banerji, R. (1986). Learning concepts by asking questions. In Machine learning: An artificial intelligence approach, Vol. 2. Morgan Kaufmann.