Toward robust real-world inference: A new perspective on Explanation-Based Learning

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

Volumn 4212 LNAI, Issue , 2006, Pages 102-113

  Dejong, Gerald ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMBINATORIAL MATHEMATICS; FORMAL LOGIC; KNOWLEDGE REPRESENTATION; ROBUSTNESS (CONTROL SYSTEMS); STATISTICAL METHODS;

NONLINEAR INTERACTION; SYMBOLIC LOGIC;

LEARNING SYSTEMS;

EID: 33750364785     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11871842_14     Document Type: Conference Paper

References (27)

1. S.D. Bay, D. G. Shapiro, and P. Langley. Revising engineering models: Combining computational discovery with knowledge. In Thirteenth European Conference on Machine Learning, pages 10-22, 2002.
2. Clifford Brunk and Michael J. Pazzani. A lexical based semantic bias for theory revision. In ICML, pages 81-89, 1995.
3. Peter Clark and Stan Matwin. Using qualitative models to guide inductive learning. In ICML, pages 49-56, 1993.
4. William W. Cohen. Incremental abductive ebl. Machine Learning, 15(1):5-24, 1994.
5. James Cussens. Parameter estimation in stochastic logic programs. Machine Learning, 44(3):245-271, 2001.
6. Gerald DeJong and Raymond Mooney. Explanation-based learning: An alternative view. Machine Learning, 1(2): 145-176, 1986.
7. Luc DeRaedt and Kristian Kersting. Probabilistic logic learning. SIGKDD Explorations, 5(1):31-48, 2003.
8. A. Epshteyn, M. Garzaran, G. DeJong, D. Padua, G Ren, X. Li, K. Yotov, and K. Pingali. Analytic models and empirical search. In The Eighteenth International Workshop on Languages and Compilers for Parallel Computing, 2005.
9. Michael Genesereth and Nils Nilsson. Logical Foundations of Artificial Intelligence. Kaufmann, Los Altos, CA, 1987.
10. Russell Greinter. The complexity of theory revision. Artificial Intelligence, 107(2):175-217, 1999.
11. Peter Haddawy. Generating bayesian networks from probablity logic knowledge bases. In UAI, pages 262-269, 1994.
12. Niels Landwehr, Kristian Kersting, and Luc De Raedt. nfoil: Integrating naïve bayes and foil. In AAAI, pages 795-800, 2005.
13. Nada Lavrac and Saso Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, New York, 1994.
14. Tom Mitchell, Richard Keller, and Smadar Kedar-Cabelli. Explanation-based learning: A unifying view. Machine Learning, 1(1):47-80, 1986.
15. Raymond Mooney and Scott Bennett. A domain independent explanation-based generalizer. In AAAI, pages 551-555, 1986.
16. Katharina Morik, Peter Brockhausen, and Thorsten Joachims. Combining statistical learning with a knowledge-based approach - a case study in intensive care monitoring. In ICML, pages 268-277, 1999.
17. Stephen Muggleton and Luc De Raedt. Inductive logic programming: Theory and methods. J. Log. Program., 19/20:629-679, 1994.
18. C. Nedellec, C. Rouveirol, H. Ade, F. Bergadano, and B. Tausend. Declarative bias in ILP. In L. De Raedt, editor, Advances in Inductive Logic Programming, pages 82-103. IOS Press, Amsterdam, 1996.
19. Dirk Ourston and Raymond Mooney. Theory refinement combining analytical and empirical methods. Artif. Intell., 66(2):273-309, 1994.
20. David Plaisted and Yunshan Zhu. The Efficiency of Theorem Proving Strategies. Vieweg & Sohn, Wiesbaden, 1999.
21. Luc De Raedt, Thomas Dietterich, Lise Getoor, and Stephen H. Muggleton, editors. Probabilistic, Logical and Relational Learning, Dagstuhl Seminar Proceedings, 2005.
22. Luc De Raedt and Stefan Kramer, editors. Workshop on Attribute-Value and Relational Learning: Crossing the Boundaries at ICML 2000, 2000.
23. Dan Roth. On the hardness of approximate reasoning. In IJCAI, pages 613-619, 1993.
24. S. J. Russell and B. N. Grosof. A declarative approach to bias in concept learning. In Proc. of AAAI-87, pages 505-510, Seattle, WA, 1987.
25. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs, NJ, 2nd edition edition, 2003.
26. Lorenza Saitta, Marco Botta, and Filippo Neri. Multistrategy learning and theory revision. Machine Learning, 11 (2-3): 153-172, 1993.
27. Geoffrey G. Towell and Jude W. Shavlik. Knowledge-based artificial neural networks. Artif. Intell., 70(1-2):119-165, 1994.