Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Generalisation Through Negation and Predicate Invention (2301.07629v4)

Published 18 Jan 2023 in cs.AI

Abstract: The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP) approach that combines negation and predicate invention. Combining these two features allows an ILP system to generalise better by learning rules with universally quantified body-only variables. We implement our idea in NOPI, which can learn normal logic programs with predicate invention, including Datalog programs with stratified negation. Our experimental results on multiple domains show that our approach can improve predictive accuracies and learning times.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (48)
  1. Arithmetic classification of perfect models of stratified programs. Fundam. Informaticae, 14(3): 339–343.
  2. Learning from positive and unlabeled data: a survey. Mach. Learn., 109(4): 719–760.
  3. From SMT to ASP: Solver-Based Approaches to Solving Datalog Synthesis-as-Rule-Selection Problems. Proc. ACM Program. Lang., 7(POPL): 185–217.
  4. Top-Down Induction of First-Order Logical Decision Trees. Artif. Intell., 101(1-2): 285–297.
  5. Occam’s Razor. Inf. Process. Lett., 24(6): 377–380.
  6. Grounding Compositional Hypothesis Generation in Specific Instances. In Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018.
  7. Clark, K. L. 1977. Negation as Failure. In Logic and Data Bases, Symposium on Logic and Data Bases, Centre d’études et de recherches de Toulouse, France, 1977, 293–322. New York.
  8. Inductive Logic Programming in Answer Set Programming. In Inductive Logic Programming - 21st International Conference, volume 7207, 91–97.
  9. Inductive logic programming at 30. Mach. Learn., 111(1): 147–172.
  10. Learning Logic Programs by Combining Programs. In ECAI 2023 - 26th European Conference on Artificial Intelligence, volume 372 of Frontiers in Artificial Intelligence and Applications, 501–508. IOS Press.
  11. Learning programs by learning from failures. Mach. Learn., 110(4): 801–856.
  12. Learning efficient logic programs. Mach. Learn., 108(7): 1063–1083.
  13. Logical reduction of metarules. Mach. Learn., 109(7): 1323–1369.
  14. Abductive Knowledge Induction from Raw Data. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, 1845–1851.
  15. Complexity and expressive power of logic programming. ACM Comput. Surv., 33(3): 374–425.
  16. Learning Non-Monotonic Logic Programs: Learning Exceptions. In Machine Learning: ECML-95, 8th European Conference on Machine Learning 1995, volume 912.
  17. Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction. In NeurIPS 2018, 7816–7826.
  18. Learning Explanatory Rules from Noisy Data. J. Artif. Intell. Res., 61: 1–64.
  19. Ferilli, S. 2016. Predicate invention-based specialization in Inductive Logic Programming. J. Intell. Inf. Syst., 47(1): 33–55.
  20. Normal Programs and Multiple Predicate Learning. In Page, D., ed., Inductive Logic Programming, 8th International Workshop, ILP-98, Madison, Wisconsin, USA, July 22-24, 1998, Proceedings, volume 1446 of Lecture Notes in Computer Science, 175–184. Springer.
  21. Answer Set Solving in Practice. Morgan & Claypool Publishers.
  22. Multi-shot ASP solving with clingo. Theory Pract. Log. Program., 19(1): 27–82.
  23. Neuro-Symbolic Hierarchical Rule Induction. In International Conference on Machine Learning, ICML 2022, volume 162, 7583–7615. PMLR.
  24. Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, 2312–2318.
  25. Learning Extended Logic Programs. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI 97, Nagoya, Japan, August 23-29, 1997, 2 Volumes, 176–181.
  26. Learning from interpretation transition. Mach. Learn., 94(1): 51–79.
  27. Meta-Interpretive Learning Using HEX-Programs. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, 6186–6190.
  28. Inductive Learning of Answer Set Programs. In Logics in Artificial Intelligence - 14th European Conference, JELIA 2014, volume 8761, 311–325.
  29. Lloyd, J. W. 2012. Foundations of logic programming. Springer Science & Business Media.
  30. Muggleton, S. 1991. Inductive Logic Programming. New Generation Computing, 8(4): 295–318.
  31. Muggleton, S. 1995. Inverse Entailment and Progol. New Generation Comput., 13(3&4): 245–286.
  32. Meta-interpretive learning of higher-order dyadic Datalog: predicate invention revisited. Mach. Learn., 100(1): 49–73.
  33. Plotkin, G. 1971. Automatic Methods of Inductive Inference. Ph.D. thesis, Edinburgh University.
  34. Learning Higher-Order Logic Programs From Failures. In IJCAI 2022.
  35. Quinlan, J. R. 1990. Learning Logical Definitions from Relations. Mach. Learn., 5: 239–266.
  36. Provenance-guided synthesis of Datalog programs. Proc. ACM Program. Lang., 4(POPL): 62:1–62:27.
  37. Ray, O. 2009. Nonmonotonic abductive inductive learning. J. Applied Logic, 7(3): 329–340.
  38. Sakama, C. 2001. Nonmonotonic Inductive Logic Programming. In Eiter, T.; Faber, W.; and Truszczynski, M., eds., Logic Programming and Nonmonotonic Reasoning, 6th International Conference, LPNMR 2001, Vienna, Austria, September 17-19, 2001, Proceedings, volume 2173 of Lecture Notes in Computer Science, 62–80. Springer.
  39. Brave induction: a logical framework for learning from incomplete information. Mach. Learn., 76(1): 3–35.
  40. Shapiro, E. Y. 1983. Algorithmic Program DeBugging. Cambridge, MA, USA: MIT Press. ISBN 0262192187.
  41. Synthesizing Datalog Programs using Numerical Relaxation. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, 6117–6124.
  42. Was the Year 2000 a Leap Year? Step-Wise Narrowing Theories with Metagol. In Riguzzi, F.; Bellodi, E.; and Zese, R., eds., ILP 2018, 141–156. Springer International Publishing.
  43. Predicate Invention for Bilevel Planning.
  44. Srinivasan, A. 2001. The ALEPH manual. Machine Learning at the Computing Laboratory, Oxford University.
  45. Distinguishing exceptions from noise in non-monotonic learning. In Proceedings of the Second Inductive Logic Programming Workshop, 97–107. Tokyo.
  46. Parameter Screening and Optimisation for ILP using Designed Experiments. J. Mach. Learn. Res., 12: 627–662.
  47. Stahl, I. 1995. The Appropriateness of Predicate Invention as Bias Shift Operation in ILP. Mach. Learn., 20(1-2): 95–117.
  48. QuickFOIL: Scalable Inductive Logic Programming. Proc. VLDB Endow., 8(3): 197–208.
Citations (3)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now