Learning from entailment of logic programs with local variables

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

Volumn 1501, Issue , 1998, Pages 143-157

  Krishna Rao, M R K ^a   Sattar, A ^a  

^a GRIFFITH UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY TREES; LOGIC PROGRAMMING; POLYNOMIAL APPROXIMATION; TREES (MATHEMATICS);

EXACT LEARNING; EXPONENTIATIONS; INCREMENTAL LEARNING; LEARNING FROM ENTAILMENTS; LOCAL VARIABLES; NATURAL NUMBER; POLYNOMIAL-TIME ALGORITHMS; TREE TRAVERSAL PROGRAMS;

COMPUTER CIRCUITS;

EID: 84961319320     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-49730-7_11     Document Type: Conference Paper

References (18)

1. D. Angluin (1988), Learning with hints, Proc. COLT'88, pp. 223-237.
2. D. Angluin (1988), Queries and concept learning, Machine Learning 2, pp. 319-342.
3. H. Arimura (1997), Learning acyclic first-order Horn sentences from entailment, Proc. ALT'97, Lecture Notes in Artificial intelligence 1316, pp. 432-445.
4. W. Cohen and H. Hirsh (1992), Learnability of description logics, Proc. COLT'92, pp. 116-127.
5. S. Dzeroski, S. Muggleton and S. Russel (1992), PAC-learnability of determinate logic programs, Proc. of COLT'92, pp. 128-135.
6. M. Frazier and L. Pitt (1993), Learning from entailment: an application to propositional Horn sentences, Proc. ICML'93, pp. 120-127.
7. M. Frazier and L. Pitt (1994), CLASSIC learning, Proc. COLT'94, pp. 23-34.
8. P. Idestam-Almquist (1996), Efficient induction of recursive definitions by structural analysis of saturations, pp. 192-205 in L. De Raedt (ed.), Advances in inductive logic programming, IOS Press.
9. M.R.K. Krishna Rao (1995), Incremental Learning of Logic Programs, Proc. of Algorithmic Learning Theory, ALT'95, LNCS 997, pp. 95-109. Revised version in Theoretical Computer Science special issue on ALT'95, Vol 185, 193-213.
10. J. W. Lloyd (1987), Foundations of Logic Programming, Springer-Verlag.
11. S. Muggleton and L. De Raedt (1994), Inductive logic programming: theory and methods, J. Logic Prog. 19/20, pp. 629-679.
12. S.H. Nienhuys-Cheng and R. de Wolf (1995), The subsumption theorem for several forms of resolution, Tech. Rep. EUR-FEW-CS-96-14, Erasmus University, Rotterdam.
13. C.D. Page and A.M. Frish (1992), Generalization and learnability: a study of constrained atoms, in Muggleton (ed.) Inductive Logic programming, pp. 29-61.
14. C. Reddy and P. Tadepalli (1998), Learning first order acyclic Horn programs from entailment, to appear in Proc. of International Conference on Machine Learning, ICML 98.
15. C. Rouveirol (1992), Extensions of inversion of resolution applied to theory completion, in Muggleton (ed.) Inductive Logic programming, pp. 63-92.
16. E. Shapiro (1981), Inductive inference of theories from facts, Tech. Rep., Yale Univ.
17. E. Shapiro (1983), Algorithmic Program Debugging, MIT Press.
18. L. Sterling and E. Shapiro (1994), The Art of Prolog, MIT Press.