Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-24T20:49:41.997Z Has data issue: false hasContentIssue false
  • English
  • Français

Iterative Learning of Answer Set Programs from Context Dependent Examples

Published online by Cambridge University Press:  14 October 2016

MARK LAW ,
ALESSANDRA RUSSO  and
KRYSIA BRODA
MARK LAW
Affiliation:
Department of Computing, Imperial College London, SW7 2AZ (e-mail: [email protected], [email protected], [email protected])
ALESSANDRA RUSSO
Affiliation:
Department of Computing, Imperial College London, SW7 2AZ (e-mail: [email protected], [email protected], [email protected])
KRYSIA BRODA
Affiliation:
Department of Computing, Imperial College London, SW7 2AZ (e-mail: [email protected], [email protected], [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy.

Keywords

Non-monotonic Inductive Logic ProgrammingAnswer Set ProgrammingIterative Learning
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 16 , Special Issue 5-6: 32nd International Conference on Logic Programming , September 2016 , pp. 834 - 848
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Athakravi, D., Corapi, D., Broda, K. and Russo, A. 2014. Learning through hypothesis refinement using answer set programming. In Inductive Logic Programming. Springer, 31–46.Google Scholar
Corapi, D., Russo, A. and Lupu, E. 2012. Inductive logic programming in answer set programming. In Inductive Logic Programming. Springer, 91–97.Google Scholar
Inoue, K., Ribeiro, T. and Sakama, C. 2014. Learning from interpretation transition. Machine Learning 94, 1, 51–79.Google Scholar
Katzouris, N., Artikis, A. and Paliouras, G. 2015. Incremental learning of event definitions with inductive logic programming. Machine Learning 100, 2–3, 555–585.CrossRefGoogle Scholar
Law, M., Russo, A. and Broda, K. 2014. Inductive learning of answer set programs. In Logics in Artificial Intelligence (JELIA 2014). LNAI, vol. 8761. Springer.Google Scholar
Law, M., Russo, A. and Broda, K. 2015. Learning weak constraints in answer set programming. Theory and Practice of Logic Programming 15, 4–5, 511–525.Google Scholar
Muggleton, S. 1991. Inductive logic programming. New Generation Computing 8, 4, 295–318.Google Scholar
Muggleton, S. 1995. Inverse entailment and Progol. New Generation Computing 13, 3–4, 245–286.Google Scholar
Muggleton, S. H., Lin, D., Pahlavi, N. and Tamaddoni-Nezhad, A. 2014. Meta-interpretive learning: application to grammatical inference. Machine Learning 94, 1, 25–49.Google Scholar
Poxrucker, A., Bahle, G. and Lukowicz, P. 2014. Towards a real-world simulator for collaborative distributed learning in the scenario of urban mobility. In Proceedings of the Eighth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops. IEEE Computer Society, 44–48.Google Scholar
Ray, O. 2009. Nonmonotonic abductive inductive learning. Journal of Applied Logic 7, 3, 329–340.Google Scholar
Ray, O., Broda, K. and Russo, A. 2003. Hybrid abductive inductive learning: A generalisation of Progol. In Inductive Logic Programming. Springer, 311–328.CrossRefGoogle Scholar
Sakama, C. 2005. Induction from answer sets in nonmonotonic logic programs. ACM Transactions on Computational Logic (TOCL) 6, 2, 203–231.CrossRefGoogle Scholar
Sakama, C. and Inoue, K. 2009. Brave induction: a logical framework for learning from incomplete information. Machine Learning 76, 1, 3–35.Google Scholar
Srinivasan, A. 2001. The aleph manual. Machine Learning at the Computing Laboratory, Oxford University.Google Scholar
Supplementary material: PDF

Law supplementary material

Online Appendix

Download Law supplementary material(PDF)
PDF 265.2 KB