Learning Recursive Theories in the Normal ILP Setting

Fundamenta Informaticae

Volumn 57, Issue 1, 2003, Pages 39-77

  Malerba, Donato ^a  

^a UNIVERSITY OF BARI   (Italy)

Author keywords

Inductive logic programming (ILP); Learning recursive theories; Machine learning; Multiple predicate learning

Indexed keywords

COMPUTATION THEORY; GRAPH THEORY; LOGIC PROGRAMMING; RECURSIVE FUNCTIONS;

INDUCTIVE LOGIC PROGRAMMING (ILP); LEARNING RECURSIVE THEORIES;

LEARNING SYSTEMS;

EID: 0344513894     PISSN: 01692968     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (66)

1. Aha, D. W., Lapointe, S., Ling, C. X., Matwin, S.: Learning recursive relations with randomly selected small training sets, Proc. Eleventh International Conference on Machine Learning, 1994.
2. Apt, K. R.: Logic programming, in: Handbook of Theoretical Computer Science (J. van Leeuwen, Ed.), vol. B, Elsevier, Amsterdam, 1990, 493-574.
3. Bergadano, F., Gunetti, D.: Learning clauses by tracing derivations, in: Proc. of the Fourth International Workshop on Inductive Logic Programming (S. Wrobel, Ed.), vol. 237 of GMD-Studien, 1994.
4. Bergadano, F., Gunetti, D.: Inductive Logic Programming: from machine learning to software engineering, The MIT Press, Cambridge, MA, 1996.
5. Boström, H.: Specialization of recursive predicates, in: Machine Learning: ECML-95 (N. Lavrač, S. Wrobel, Eds.), vol. 912 of LNAI, Springer-Verlag, Berlin, 1995, 92-106.
6. Boström, H.: Induction of Recursive Transfer Rules, in: Learning Language in Logic (J. Cussens, S. Džeroski, Eds.), vol. 1925 of LNAI, Springer-Verlag, Berlin, 2000, 237-246.
7. Bratko, I.: Applications of machine learning: towards knowledge synthesis, New Generation Computing, 11, 1993, 343-360.
8. Buntine, W.: Generalised subsumption and its applications to induction and redundancy, Artificial Intelligence, 36, 1988, 149-176.
9. Cameron-Jones, R. M., Quinlan, J. R.: Avoiding pitfalls when learning recursive theories, Proc. Twelfth International Joint Conference on Artificial Intelligence, 1993.
10. Ceri, S., Gottlob, G., L.Tanca: What you always wanted to know about Datalog (and never dared to ask), IEEE Transactions on Knowledge and Data Engineering, 1(1), 1989, 146-166.
11. Cohen, W. W.: Learnability of restricted logic programs, Proc. 3rd International Workshop on Inductive Logic Programming (S. Muggleton, Ed.), 1993.
12. De Raedt, L.: Interactive Theory Revision, Academic Press, London, 1992.
13. De Raedt, L., Dehaspe, L.: Clausal discovery, Machine Learning Journal, 26(2/3), 1997, 99-146.
14. De Raedt, L., Lavrač, N.: The many faces of inductive logic programming, in: Methodologies for Intelligent Systems (J. Komorowski, Z.W.Raś, Eds.), vol. 689 of LNAI, Springer-Verlag, 1993, 435-449.
15. De Raedt, L., Lavrač, N.: Multiple predicate learning in two Inductive Logic Programming settings, Journal on Pure and Applied Logic, 4(2), 1996, 227-254.
16. De Raedt, L., Lavrač, N., Džeroski, S.: Multiple predicate learning, Proc. 13th International Joint Conference on Artificial Intelligence, 1993.
17. Džeroski, S., Bratko, I.: Handling Noise in Inductive Logic Programming, Proc. Second International Workshop on Inductive Logic Programming, Institute for New Generation Computing Technology, 1992.
18. Džeroski, S., Lavrač, N.: Relational Data Mining, Springer-Verlag, Berlin, 2001.
19. Esposito, F., D.Malerba, Lisi, F. A.: Induction of recursive theories in the normal ILP setting: issues and solutions, in: Inductive Logic Programming (J. Cussens, A. Frisch, Eds.), vol. 1866 of LNAI, Springer-Verlag, Berlin, 2000, 93-111.
20. Esposito, F., Malerba, D., Lisi, F. A.: Machine Learning for Intelligent Processing of Printed Documents, Journal of Intelligent Information Systems, 14(2/3), 2000, 175-198.
21. Esposito, F., Malerba, D., Marengo, V.: Inductive Learning from Numerical and Symbolic Data: An Integrated Framework, Intelligent Data Analysis, 5(6), 2001, 445-461.
22. Esposito, F., Semeraro, G., Fanizzi, N., Ferilli, S.: Multistrategy Theory Revision: Induction and Abduction in INTHELEX, Machine Learning Journal, 38(1/2), 2000, 133-156.
23. Flener, P., Yilmaz, S.: Inductive synthesis of recursive logic programs: achievements and prospects, Journal of Logic Programming, Special Issue on Synthesis, Transformation, and Analysis, 41(2-3), 1999, 141-195.
24. Fogel, L., Zaverucha, G.: Normal Programs and Multiple Predicate Learning, in: Inductive Logic Programming (D. Page, Ed.), vol. 1446 of LNAI, Springer-Verlag, 1998, 175-184.
25. Gelfond, M., Lifschitz, V.: Logic programs with classical negation, Proc. of the Seventh International Logic Programming Conference, MIT Press, Cambridge, 1990.
26. Giordana, A., Saitta, L., Baroglio, C.: Learning simple recursive theories, in: Methodologies for Intelligent Systems (J. Komorowski, Z. Raś, Eds.), vol. 689 of LNAI, Springer-Verlag, 1993, 425-434.
27. Gottlob, G.: Subsumption and Implication, Information Processing Letters, 24(2), 1987, 109-111.
28. Idestam-Almquist, P.: Generalization of clauses, Ph.D. Thesis, Department of Computer and Systems Sciences, Stockholm University and Royal Institute of Technology, Stockholm, Sweden, 1993.
29. Idestam-Almquist, P.: Efficient induction of recursive definitions by structural analysis of saturations, in: Advances in Inductive Logic Programming (L. De Raedt, Ed.), IOS Press, Amsterdam, 1996, 192-205.
30. Jorge, A., Brazdil, P.: Architecture for iterative learning of recursive definitions, in: Advances in Inductive Logic Programming (L. De Raedt, Ed.), IOS Press, Amsterdam, 1996, 206-218.
31. Khardon, R.: Learning to take Actions, Machine Learning Journal, 35(1), 1999, 57-90.
32. Khardon, R.: Learning Horn Expressions with LogAn-H, Proc. of the Seventeenth International Conference on Machine Learning, 2000.
33. Lamma, E., Mello, P., Milano, M., Riguzzi, F.: Integrating Extensional and Intensional ILP Systems through Abduction, Proc. of the Seventh International Workshop on Logic Program Synthesis and Transformation (LOPSTR'97), 1997.
34. Lapointe, S., Matwin, S.: Sub-unification: A tool for efficient induction of recursive programs, Proc. of the Ninth International Conference on Machine Learning, Morgan Kaufmann, Aberdeeen, 1992.
35. Lavrač, N., Džeroski, S.: Inductive Logic Programming: techniques and applications, Ellis Horwood, Chichester, 1994.
36. Lavrač, N., Džeroski, S., Bratko, I.: Handling imperfect data in inductive logic programming, in: Advances in Inductive Logic Programming (L. De Raedt, Ed.), IOS Press, Amsterdam, 1996, 48-64.
37. van Leeuwen, J.: Graph Algorithms, vol. A of Handbook of Theoretical Computer Science, Elsevier, Amsterdam, 1990, 525-631.
38. Levi, G., Sirovich, F.: Generalized and-or graphs, Artificial Intelligence, 7, 1976, 243-259.
39. Lloyd, J. W.: Foundations of Logic Programming, Second edition, Springer-Verlag, Berlin, 1987.
40. Lorenzo, D., Otero, R. P.: Learning to reason about actions, Proc. of the Fourteenth European Conference on Artificial Intelligence (W. Horn, Ed.), IOS Press, Amsterdam, 2000.
41. Malerba, D., Esposito, F., Lanza, A., Lisi, F.: Machine learning for information extraction from topographic maps, in: Geographic Data Mining and Knowledge Discovery (H. J. Miller, J. Han, Eds.), Taylor and Francis, London, 2001, 291-314.
42. Malerba, D., Esposito, F., Lisi, F.: Learning Recursive Theories with ATRE, Proc. Thirteenth European Conference on Artificial Intelligence (H. Prade, Ed.), John Wiley & Sons, Chichester, 1998.
43. Malerba, D., Esposito, F., Semeraro, G., Caggese, S.: Learning simple recursive theories, in: AI*IA 97: Advances in Artificial Intelligence (M. Lenzerini, Ed.), vol. 1321 of LNAI, Springer-Verlag, 1997, 24-35.
44. Malerba, D., Semeraro, G., Esposito, F.: A multistrategy approach to learning multiple dependent concepts, in: Machine Learning and Statistics: The interface (G. Nakhaeizadeh, C. Taylor, Eds.), John Wiley & Sons, New York, 1997, 87-106.
45. Martin, L., Vrain, C.: A three-valued framework for the induction of general logic programs, in: Advances in Inductive Logic Programming (L. De Raedt, Ed.), IOS Press, Amsterdam, 1996, 219-235.
46. Michalski, R.: A theory and methodology of inductive learning, in: Machine Learning - An Artificial Intelligence Approach (R. Michalski, J. Carbonell, T. Mitchell, Eds.), Tioga Publishing Co., Palo Alto, CA, 1983, 83-133.
47. Mitchell, T.: Machine Learning, MCGraw-Hill, 1997.
48. Mofizur, C., Numao, M.: Top-down induction of recursive programs from small number of sparse examples, in: Advances in Inductive Logic Programming (L. De Raedt, Ed.), IOS Press, Amsterdam, 1996, 236-253.
49. Muggleton, S.: Inductive Logic Programming, Academic Press, London, 1992.
50. Muggleton, S.: Inverting Implication, Proceedings of the 2nd International Workshop on Inductive Logic Programming (S. Muggleton, K. Furukawa, Eds.), 1992.
51. Muggleton, S.: Inverse Entailment and Progol, New Generation Computing, 13(3/4), 1995, 245-286.
52. Muggleton, S., Bryant, C.: Theory completion using inverse entailment, in: Inductive Logic Programming (J. Cussens, A. Frisch, Eds.), vol. 1866 of LNAI, Springer-Verlag, 2000, 130-146.
53. Nagy, G., Seth, S., Stoddard, S.: A prototype document image analysis system for technical journals, IEEE Computer, 25(7), 1992, 10-22.
54. Nienhuys-Cheng, S.-W., de Wolf, R.: A complete method for program specialization based upon unfolding, Proc. Twelfth European Conference on Artificial Intelligence, 1996.
55. Nienhuys-Cheng, S.-W., de Wolf, R.: The Subsumption theorem in inductive logic programming: Facts and fallacies, in: Advances in Inductive Logic Programming (L. De Raedt, Ed.), IOS Press, Amsterdam, 1996, 265-276.
56. Nienhuys-Cheng, S.-W., de Wolf, R.: Foundations of inductive logic programming, Springer, Heidelberg, 1997.
57. Pazzani, M., Kibler, D.: The utility of knowledge in inductive learning, Machine Learning Journal, 9, 1992, 57-94.
58. Plotkin, G.: A note on inductive generalization, vol. 5 of Machine Intelligence, Edinburgh University Press, Edinburgh, 1970, 153-163.
59. Plotkin, G.: A further note on inductive generalization, vol. 6 of Machine Intelligence, Edinburgh University Press, Edinburgh, 1971, 101-124.
60. Ramakrishnan, R., Srivastava, D., Sudarshan, S.: Rule ordering in bottom-up fixpoint evaluation of logic programs, IEEE Transactions on Knowledge and Data Engineering, 6, 1994, 501-517.
61. Rouveirol, C.: Flattening and saturation: Two representation changes for generalization, Machine Learning Journal, 14(2), 1994, 219-232.
62. Sommer, E.: FENDER: an approach to theory restructuring (extended abstract), in: Machine Learning: ECML-95 (N. Lavrač, S. Wrobel, Eds.), vol. 912 of LNAI, Springer-Verlag, 1994, 356-359.
63. Sommer, E.: An approach to quantifying the quality of induced theories, Proc. IJCAI Workshop on Machine Learning and Comprehensibility (C. Nedellec, Ed.), 1995.
64. Toussaint, J., Schmid, U., Wysotzki, F.: Using recursive control rules in planning, Proc. of ICAI Session on Learning and Adapting in AI Planning, Las Vegas, 2001.
65. Van Laer, W., De Raedt, L., Džeroski, S.: On Multi-Class Problems and Discretization in Inductive Logic Programming, in: Proceedings of the 10th International Symposium on Methodologies for Intelligent Systems (ISMIS97) (Z. Raś, A. Skowron, Eds.), vol. 1325 of LNAI, Springer-Verlag, 1997, 277-286.
66. Zelezny, F., Miksovsky, P., Stepankova, O., Zidek, J.: ILP for Automated Telephony, Inductive Logic Programming (Work in Progress) (J. Cussens, A. Frisch, Eds.), 2000.