R. Mike Cameron-Jones
Skip Abstract Section
Abstract
FOIL is a system for inducing function-free Horn clause definitions of relations from example and extensionally defined background relations. It demonstrates the successful application of a general to specific approach to clause induction using heuristically guided search. This paper describes the current version of FOIL, assesses its performance and notes areas for improvement. The successful application of similar methods in other systems is reviewed to demonstrate their general utility.
- K. M. Ali and M. Pazzani. HYDRA: A noise-tolerant relational concept learning algorithm. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 1064--1070. Morgan Kaufmann, 1993.Google Scholar
- S. Bell and S. Weber. On the close logical relationship between FOIL and the frameworks of Helft and Plotkin. In Proceedings of the Third International Workshop on Inductive Logic Programming: ILP'93, pages 1--10. J. Stefan Institute, 1993.Google Scholar
- I. Bratko. Prolog Programming for Artificial Intelligence. Addison-Wesley, 1986. Google ScholarDigital Library
- R. M. Cameron-Jones and J. R. Quinlan. First order learning, zeroth order data. In Proceedings of the Sixth Australian Joint Conference on Artificial Intelligence. World Scientific, 1993, (to appear).Google Scholar
- L. De Raedt, N. Lavrač, and S. Džeroski. Multiple predicate learning. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 1037--1042. Morgan Kaufmann, 1993.Google Scholar
- D. De Schreye and K. Verschaetse. Tutorial on termination of logic programs. In Meta-Programming in Logic, Proceedings Third International Workshop, META-92, pages 70--88. Springer Verlag, 1992. Google ScholarDigital Library
- B. Kijsirikul, M. Numao, and M. Shimura. Efficient learning of logic programs with non-determinate, non-discriminating literals. In Proceedings of the Eighth International Workshop of Machine Learning, pages 417--421. Morgan Kaufmann, 1991.Google ScholarDigital Library
- B. Kijsirikul, M. Numao, and M. Shimura. Discrimination based constructive induction of logic programs. In Proceedings of the Tenth National Conference on Artificial Intelligence, pages 44--49. MIT Press, 1992.Google ScholarDigital Library
- N. Lavrač and S. Džeroski. Inductive learning of relations from noisy examples. In S. Muggleton, editor, Inductive Logic Programming, pages 495--516. Academic Press, 1992.Google Scholar
- C. Leckie and I. Zukerman. An inductive approach to learning search control rules for planning. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 1100--1105. Morgan Kaufmann, 1993.Google Scholar
- S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the First Conference on Algorithmic Learning Theory, Tokyo, 1990. Omsha.Google Scholar
- M. Pazzani and D. Kibler. The utility of knowledge in inductive learning. Machine Learning, 9:57--94, 1992. Google ScholarDigital Library
- J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81--106, 1986. Google ScholarCross Ref
- J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239--266, 1990. Google ScholarDigital Library
- J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993. The Morgan Kaufmann Series in Machine Learning. Google ScholarDigital Library
- J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. In Proceedings of the European Conference on Machine Learning, pages 3--20. Springer Verlag, 1993. Google ScholarDigital Library
- J. Rissanen. Stochastic Complexity in Statistical Inquiry. World Scientific, 1989. Series in Computer Science - Vol. 15. Google ScholarDigital Library
- Y. Sagiv. A termination test for logic programs. In Proceedings of the 1991 International Symposium on Logic Programming. MIT Press, 1991.Google Scholar
- E. Y. Shapiro. Algorithmic Program Debugging. MIT Press, 1983. Google ScholarDigital Library
- G. Silverstein and M. J. Pazzani. Relational clichés: Constraining constructive induction during relational learning. In Proceedings of the Eighth International Workshop on Machine Learning (ML91), pages 203--207. Morgan Kaufmann, 1991.Google Scholar
- I. Stahl, B. Tausend, and R. Wirth. General-to-specific learning of horn clauses from positive examples. In Proceedings of Comp Euro 1992, pages 436--441. IEEE, 1992.Google ScholarCross Ref
- I. Stahl, B. Tausend, and R. Wirth. Two methods for improving inductive logic programming systems. In Proceedings of the European Conference on Machine Learning, pages 41--55. Springer Verlag, 1993. Google ScholarDigital Library
- S. Tangkitvanich and M. Shimura. Theory refinement based on the minimum description length principle. Technical report, Department of Computer Science, Tokyo Institute of Technology, 1993.Google Scholar
- J. Wogulis and M. J. Pazzani. A methodology for evaluating theory revision systems: Results with Audrey II. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 1128--1134. Morgan Kaufmann, 1993.Google Scholar
- J. M. Zelle and R. J. Mooney. Combining FOIL and EBG to speed-up logic programs. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 1106--1111. Morgan Kaufmann, 1993.Google Scholar
Index Terms
- Efficient top-down induction of logic programs
Recommendations
Induction from answer sets in nonmonotonic logic programs
Inductive logic programming (ILP) realizes inductive machine learning in computational logic. However, the present ILP mostly handles classical clausal programs, especially Horn logic programs, and has limited applications to learning nonmonotonic logic ...
Comments