Induction of logic programs: FOIL and related systems

Abstract

FOIL is a first-order learning system that uses information in a collection of relations to construct theories expressed in a dialect of Prolog. This paper provides an overview of the principal ideas and methods used in the current version of the system, including two recent additions. We present examples of tasks tackled by FOIL and of systems that adapt and extend its approach.

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References

  1. 1)

    Aha, D.W., Lapointe, S., Ling, C.X., and Matwin, S., “Learning Recursive Relations with Randomly-Selected Small Training Sets,”Proceedings Eleventh International Conference on Machine Learning, New Brunswick, New Jersey, San Francisco: Morgan Kaufmann, pp. 12–18, 1994.

    Google Scholar 

  2. 2)

    Ali, K. and Pazzani, M.J., “HYDRA: A Noise-Tolerant Relational Concept Learning Algorithm,”Proceedings Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, San Francisco: Morgan Kaufmann, pp. 1064–1070, 1993.

    Google Scholar 

  3. 3)

    Bain, M.E., “Learning Logical Exceptions in Chess,”Ph.D. thesis, Department of Statistics and Modelling Science, University of Strathclyde, Scotland, 1994.

    Google Scholar 

  4. 4)

    Bell, S. and Weber, S., “On the Close Logical Relationship betweenfoil and the Frameworks of Helft and Plotkin,”Proceedings Third International Workshop on Inductive Logic Programming, Bled, Slovenia, pp. 127–147, 1993.

  5. 5)

    Bratko, I.,Prolog Programming for Artificial Intelligence (2nd edition), Workingham, UK: Addison-Wesley, 1990.

    Google Scholar 

  6. 6)

    Cameron-Jones, R.M. and Quinlan, J.R., “Avoiding Pitfalls When Learning Recursive Theories,”Proceedings Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, San Francisco, Morgan Kaufmann, pp. 1050–1057, 1993.

    Google Scholar 

  7. 7)

    Cameron-Jones, R.M. and Quinlan, J.R., “First Order Learning, Zeroth Order Data,”Proceedings AI’93 Australian Joint Conference on Artificial Intelligence, Melbourne, Singapore: World Scientific, pp. 316–321, 1993.

    Google Scholar 

  8. 8)

    Cameron-Jones, R.M. and Quinlan, J.R., “Efficient Top-down Induction of Logic Programs,”SIGART, 5, pp. 33–42, 1994.

    Article  Google Scholar 

  9. 9)

    Cohen, W.W., “Pac-Learning a Restricted Class of Recursive Logic Programs,”Proceedings Third International Workshop on Inductive Logic Programming, Bled, Slovenia, pp. 73–86, 1993.

  10. 10)

    Cohen, W.W., “Recovering Software Specifications with Inductive Logic Programming,”Proceedings AAAI-94 Twelfth National Conference on Artificial Intelligence, Seattle, Washington, Menlo Park: AAAI Press, pp. 142–148, 1994.

    Google Scholar 

  11. 11)

    Cohen, W.W., “Grammatically Biased Learning: Learning Logic Programs Using an Explicit Antecedent Description Language,”Artificial Intelligence, 68, pp. 303–366, 1994.

    MATH  Article  Google Scholar 

  12. 12)

    De Raedt, L., Lavrač, N., and Džeroski, S., “Multiple Predicate Learning,”Proceedings Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, San Francisco: Morgan Kaufmann, pp. 1037–1042, 1993.

    Google Scholar 

  13. 13)

    DeJong, G. and Mooney, R., “Explanation-Based Learning: An Alternative View,”Machine Learning, 1, pp. 145–176, 1986.

    Google Scholar 

  14. 14)

    Dietterich, T.G., “The Methodology of Knowledge Layers for Inducing Descriptions of Sequentially Ordered Events,”Technical Report, R-80-1024, Department of Computer Science, University of Illinois at Urbana-Champaign, USA, 1980.

    Google Scholar 

  15. 15)

    Fürnkranz, J., “Fossil: A Robust Relational Learner,”Technical Report, TR-93-28, Austrian Research Institute for Artificial Intelligence, Vienna, 1993.

    Google Scholar 

  16. 16)

    Gold, E.M., “Language Identification in the Limit,”Information and Control, 10, pp. 447–474, 1967.

    MATH  Article  Google Scholar 

  17. 17)

    John, G.S., Kohavi, R., and Pfleger, K., “Irrelevant Features and the Subset Selection Problem,”Proceedings Eleventh International Coference on Machine Learning, New Brunswick, New Jersey, San Francisco: Morgan Kaufmann, pp. 121–129, 1994.

    Google Scholar 

  18. 18)

    Kijsirikul, B., Numao, M., and Shimura, M., “Discrimination-Based Constructive Induction of Logic Programs,”Proceedings AAAI-92 Tenth National Conference on Artificial Intelligence, San Jose, CA, Menlo Park: AAAI Press, pp. 44–49, 1992.

    Google Scholar 

  19. 19)

    Lavrač, N. and Džeroski, S.,Inductive Logic Programming: Techniques and Applications, London: Ellis Horwood, 1994.

    Google Scholar 

  20. 20)

    Leckie, C. and Zukerman, I., “An Inductive Approach to Learning Search Control Rules for Planning,”Proceedings Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, San Francisco: Morgan Kaufmann, pp. 1100–1105, 1993.

    Google Scholar 

  21. 21)

    Michalski, R.S., “Pattern Recognition as Rule-Guided Inductive Inference,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 2, pp. 349–361, 1980.

    MATH  Article  Google Scholar 

  22. 22)

    Mitchell, T.M., Keller, R.M., and Kedar-Cabelli, S.T., “Explanation-Based Generalization: A Unifying View,”Machine Learning, 1, pp. 47–80, 1986.

    Google Scholar 

  23. 23)

    Moore, A.W. and Lee, M.S., “Efficient Algorithms for Minimizing Cross-Validation Error,”Proceedings Eleventh International Conference on Machine Learning, New Brunswick, New Jersey, San Francisco: Morgan Kaufmann, pp. 190–198 1994.

    Google Scholar 

  24. 24)

    Muggleton, S. and Buntine, W., “Machine Invention of First-Order Predicates by Inverting Resolution,”Proceedings Fifth International Conference on Machine Learning, Ann Arbor, Michigan, San Mateo: Morgan Kaufmann, pp. 339–352, 1988.

    Google Scholar 

  25. 25)

    Muggleton, S., Bain, M., Hayes-Michie, J., and Michie, D., “An Experimental Comparison of Human and Machine Learning Formalisms,”Proceedings of the Sixth International Machine Learning Workshop, Ithaca, NY, San Mateo: Morgan Kaufmann, pp. 113–118, 1989.

    Google Scholar 

  26. 26)

    Muggleton, S. and Feng, C., “Efficient Induction of Logic Programs,” inInductive Logic Programming (S. Muggleton, ed.), London: Academic Press, pp. 281–298, 1992.

    Google Scholar 

  27. 27)

    Muggleton, S., King, R.D., and Sternberg, M.J., “Protein Secondary Structure Prediction Using Logic-Based Machine Learning,”Protein Engineering, 5, pp. 646–657, 1992.

    Google Scholar 

  28. 28)

    Pazzani, M.J., Brunk, C.A., and Silverstein, G., “A Knowledge-Intensive Approach to Learning Relational Concepts,”Proceedings Eighth International Workshop on Machine Learning, Evanston Illinois, San Mateo: Morgan Kaufmann, pp. 432–436, 1991.

    Google Scholar 

  29. 29)

    Pazzani, M.J. and Kibler, D., “The Utility of Knowledge in Inductive Learning,”Machine Learning, 9, 1, pp. 57–94, 1992.

    Google Scholar 

  30. 30)

    Quinlan, J.R. and Rivest, R.L., “Inferring Decision Trees Using the Minimum Description Length Principle,”Information and Computation, 80, pp. 227–248, 1989.

    MATH  Article  MathSciNet  Google Scholar 

  31. 31)

    Quinlan, J.R., “Learning Logical Definitions from, Relations,”Machine Learning, 5, pp. 239–266, 1990.

    Google Scholar 

  32. 32)

    Quinlan, J.R., “Determinate Literals in Inductive Logic Programming,”Proceedings Twelfth International Joint Conference on Artificial Intelligence, Sydney, Australia, pp. 746–750, 1991.

  33. 33)

    Quinlan, J.R.,C4.5: Programs for Machine Learning, San Mateo: Morgan Kaufmann, 1993.

    Google Scholar 

  34. 34)

    Quinlan, J.R. and Cameron-Jones, R.M., “FOIL: A Midterm Report,”Proceedings European Conference on Machine Learning, Vienna, Berlin: Springer-Verlag, pp. 3–20, 1993.

    Google Scholar 

  35. 35)

    Sammut, C.A. and Banerji, R.B., “Learning Concepts by Asking Questions,” inMachine Learning: An Artificial Intelligence Approach, Vol. 2 (R.S. Michalski, J.G. Carbonell and T.M. Mitchell, eds.), Los Altos: Morgan Kaufmann, 1986.

    Google Scholar 

  36. 36)

    Sammut, C.A., “The Origins of Inductive Logic Programming: A Prehistoric Tale,”Proceedings Third International Workshop on Inductive Logic Programming, Bled, Slovenia, pp. 127–147, 1993.

  37. 37)

    Semeraro, G., Brunk, C.A., and Pazzani, M.J., “Traps and Pitfalls When Learning Logical Theories: A Case Study withFoil andFocl,”Technical Report, 93–33, Department of Information and Computer Science, University of California, Irvine, USA, 1993.

    Google Scholar 

  38. 38)

    Shapiro, E.Y.,Algorithmic Program Debugging, Cambridge, MA: MIT Press, 1983.

    Google Scholar 

  39. 39)

    Winston, P.H., “Learning Structural Descriptions from Examples,” inThe Psychology of Computer Vision (P.H. Winston, ed.), New York: McGraw-Hill, 1975.

    Google Scholar 

  40. 40)

    Wogulis, J. and Pazzani, M.J., “A Methodology for Evaluating Theory Revision Systems: Results withAudrey II,”Proceedings thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, San Francisco: Morgan Kaufmann, pp. 1128–1134, 1993.

    Google Scholar 

  41. 41)

    Zelle, J.M. and Mooney, R.J., “CombiningFoil and EBG to Speed-up Logic Programs,”Proceedings Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, San Francisco: Morgan Kaufmann, pp. 1106–1111, 1993.

    Google Scholar 

  42. 42)

    Zelle, J.M. and Mooney R.J., “Inducing Deterministic Prolog Parsers from Treebanks: A Machine Learning Approach,”Proceedings AAAI-94 Twelfth National Conference on Artificial Intelligence, Seattle, Washington, Menlo Park: AAAI Press, 1994.

    Google Scholar 

  43. 43)

    Zelle, J.M., Mooney, R.J., and Konvisser, J.B., “Combining Top-down and Bottom-up Techniques in Inductive Logic Programming,”Proceedings Eleventh International Conference on Machine Learning, New Brunswick, New Jersey, San Francisco: Morgan Kaufmann, pp. 343–351, 1994.

    Google Scholar 

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Correspondence to J. R. Quinlan.

Additional information

John Ross Quinlan: He is Professor of Computer Science at the University of Sydney. He received the BSc degree in Physics and Computing from the University of Sydney in 1965 and the PhD degree in Computer Science from the University of Washington in 1968. His research focuses on aspects of machine learning and theory simplification in both attribute-value and relational formalisms.

Richard Michael Cameron-Jones: He received his BSc(Eng) degree in electrical engineering in 1980 from Imperial College of Science and Technology, University of London, and his PhD in Artificial Intelligence in 1991 from the University of Edinburgh. He worked in the UK avionics industry before starting his PhD which was in the arca of computer vision. After the PhD he worked as a researcher in machine learning, with Professor Quinlan, before taking up his current position as a lecturer at the University of Tasmania, Launceston. His research interests in machine learning include inductive logic programming, instance based learning and minimum encoding length techniques.

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Quinlan, J.R., Cameron-Jones, R.M. Induction of logic programs: FOIL and related systems. NGCO 13, 287–312 (1995). https://doi.org/10.1007/BF03037228

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Keywords

  • Inductive Logic Programming
  • Relational Learning