Refining first order theories with neural networks

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

Volumn 1325, Issue , 1997, Pages 84-93

  Botta, M ^a   Giordana, A ^a   Piola, R ^a  

^a UNIVERSITY OF TURIN   (Italy)

Author keywords

Cormectionist learning; First order logic; Learning and knowledge discovery; Soft computing; Theory refinement

Indexed keywords

COMPUTER CIRCUITS; FORMAL LOGIC; INFORMATION MANAGEMENT; INTELLIGENT SYSTEMS; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; REFINING; SOFT COMPUTING;

CLASSIFICATION THEORY; CORMECTIONIST LEARNING; EXPERIMENTAL EVALUATION; FIRST ORDER LOGIC; FIRST ORDER THEORIES; FIRST-ORDER LOGIC LANGUAGES; RELATIONAL LEARNING SYSTEMS; THEORY REFINEMENT;

NETWORK ARCHITECTURE;

EID: 84947808583     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-63614-5_8     Document Type: Conference Paper

References (17)

1. C. Baroglio, A. Giordana, M. Kaiser, M. Nuttin, and R. Piola. Learning controllers for industrial robots. Machine Learning, 23: 221-250, July 1996.
2. H. R. Berenji. Fuzzy logic controllers. In R. R. Yager and L. A. Zadeh, editors, An Introduction to Fuzzy Logic Applications in Intelligent Systems, pages 69-96. Kluwer Academic Publishers, 1992.
3. H. R. Berenji and P. Khedkar. Learning and tuning fuzzy controllers through reinforcements. IEEE Transactions on Neural Networks, 3(5): 724-740, September 1992.
4. E. B. Blumer, A. Ehrenfeucht, D. Hausster, and M. K. Warmuth. Learnabifity and the Vapnik-Chervonenkis dimension. Journal of the A CM, 36: 929-965, 1989.
5. M. Botta, A. Giordana, and R. Piola. FONN: Combining first order logic with connectionist learning. In Proceedings of the 14 th International Conference on Machine Learning ICML-97, Nashville, TN, July 1997. Morgan Kaufmann.
6. L. Breiman, J. H. Friedman, R. A. Ohlsen, and C. J. Stone. Classification And Regression Trees. Wadsworth & Brooks, Pacific Grove, CA, 1984.
7. S. E. Fahlman and C. Lebiere. The cascade-correlation learning architecture. In D. S. Touretzky, editor, Advances in Neural Information Processing Systems 2. Morgan Kaufmann, 1990.
8. L. M. Fu. Knowledge-based connectionism for revising domain theories. IEEE Transactions on Systems, Man and Cybernetics, 23(1): 173-182, January 1993.
9. J. J. Mahoney and R. J. Mooney. Comparing methods for refining certainity-factor rule-bases. In Proc. of the Eleventh Internetional Workshop on Machine Learning ML-94, Rutgers University, N J, July 1994.
10. R. Michalski. A theory and methodology of inductive learning. In R. Michalski, J. Carbonell, and T. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, pages 83-134, Los Altos, CA, 1983. Morgan Kaufmann.
11. T. Poggio and F. Girosi. Networks for approximation and learning. Proceedings of the IEEE, 78(9): 1481-1497, September 1990.
12. R. J. Quinlan. Induction of decision trees. Machine Learning, 1: 81-106, 1986.
13. D. E. Rumelhart and J. L. McClelland. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Parts I 8J II. MIT Press, Cambridge, Massachusetts, 1986.
14. G. Towell and J. W. Shavlik. Knowledge based artificial neural networks. Artficial Intelligence, 70(4): 119-166, 1994.
15. G. G. Towell, J. W. Shavlik, and M. O. Noordwier. Refinement of approximate domain theories by knowledge-based neural networks. In AAAI'90, pages 861-866, 1990.
16. V. Tresp, J. Hollatz, and S. Ahmad. Network structuring and training using rulebased knowledge. In Advances in Neural Information Processing Systems 5 (NIPS-5), 1993.
17. L. A. Zadeh. Knowledge representation in fuzzy logic. In P. R. Yager and L. A. Zadeh, editors, An Introduction to Fuzzy Logic Applications in Intelligent Systems, pages 1-25. Kluwer Academic Publishers, 1992.