Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-14T21:23:28.315Z Has data issue: false hasContentIssue false
  • English
  • Français

Conflict Generalisation in ASP: Learning Correct and Effective Non-Ground Constraints

Published online by Cambridge University Press:  21 September 2020

RICHARD TAUPEOpen the ORCID record for RICHARD TAUPE[Opens in a new window],
ANTONIUS WEINZIERLOpen the ORCID record for ANTONIUS WEINZIERL[Opens in a new window] and
GERHARD FRIEDRICH
RICHARD TAUPE
Affiliation:
Siemens AG Österreich, (e-mail: richard.taupe@siemens.com)Alpen-Adria-Universität, Klagenfurt, Austria, (e-mail: gerhard.friedrich@aau.at)
ANTONIUS WEINZIERL
Affiliation:
TU Wien (Vienna University of Technology), Austria, (e-mail: antonius.weinzierl@kr.tuwien.ac.at)
GERHARD FRIEDRICH
Affiliation:
Alpen-Adria-Universität, Klagenfurt, Austria, (e-mail: gerhard.friedrich@aau.at)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Generalising and re-using knowledge learned while solving one problem instance has been neglected by state-of-the-art answer set solvers. We suggest a new approach that generalises learned nogoods for re-use to speed-up the solving of future problem instances. Our solution combines well-known ASP solving techniques with deductive logic-based machine learning. Solving performance can be improved by adding learned non-ground constraints to the original program. We demonstrate the effects of our method by means of realistic examples, showing that our approach requires low computational cost to learn constraints that yield significant performance benefits in our test cases. These benefits can be seen with ground-and-solve systems as well as lazy-grounding systems. However, ground-and-solve systems suffer from additional grounding overheads, induced by the additional constraints in some cases. By means of conflict minimization, non-minimal learned constraints can be reduced. This can result in significant reductions of grounding and solving efforts, as our experiments show.

Keywords

Answer Set ProgrammingDeductive LearningNon-Ground Nogood Learning

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 5: 36th International Conference on Logic Programming Special Issue I , September 2020, pp. 799 - 814
Check for updates
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2020. Published by Cambridge University Press

References

Alviano, M., Calimeri, F., Dodaro, C., Fuscà, D., Leone, N., Perri, S., Ricca, F., Veltri, P., and Zangari, J. 2017. The ASP system DLV2. In LPNMR. LNCS, vol. 10377. Springer, 215–221.Google Scholar
Bogaerts, B. and Weinzierl, A. 2018. Exploiting justifications for lazy grounding of answer set programs. In IJCAI. ijcai.org, 1737–1745.Google Scholar
Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Maratea, M., Ricca, F., and Schaub, T. 2020. ASP-Core-2 input language format. Theory Pract. Log. Program. 20, 2, 294–309.CrossRefGoogle Scholar
DeJong, G. and Mooney, R. J. 1986. Explanation-based learning: An alternative view. Mach. Learn. 1, 2, 145–176.Google Scholar
Faber, W., Pfeifer, G., and Leone, N. 2011. Semantics and complexity of recursive aggregates in answer set programming. Artif. Intell. 175, 1, 278–298.Google Scholar
Friedrich, G., Ryabokon, A., Falkner, A. A., Haselböck, A., Schenner, G., and Schreiner, H. 2011. (Re)configuration using answer set programming. In Configuration Workshop. CEUR-WS.org.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., and Schaub, T. 2014. Clingo = ASP + control: Preliminary report. CoRR abs/1405.3694.Google Scholar
Gebser, M., Kaufmann, B., and Schaub, T. 2012. Conflict-driven answer set solving: From theory to practice. Artif. Intell. 187, 52–89.Google Scholar
Gebser, M., Maratea, M., and Ricca, F. 2020. The seventh answer set programming competition: Design and results. Theory Pract. Log. Program. 20, 2, 176–204.CrossRefGoogle Scholar
van Harmelen, F. and Bundy, A. 1988. Explanation-based generalisation = partial evaluation. Artif. Intell. 36, 3, 401–412.CrossRefGoogle Scholar
Hirsh, H. 1987. Explanation-based generalization in a logic-programming environment. In IJCAI. Morgan Kaufmann, 221–227.Google Scholar
Leutgeb, L. and Weinzierl, A. 2017. Techniques for efficient lazy-grounding ASP solving. In DECLARE. LNCS, vol. 10997. Springer, 132–148.Google Scholar
Lintao, Zhang, Madigan, C. F., Moskewicz, M. H., and Malik, S. 2001. Efficient conflict driven learning in a boolean satisfiability solver. In ICCAD. IEEE, 279–285.Google Scholar
Mitchell, T. M. 1997. Machine learning, International Edition. McGraw-Hill Series in Computer Science. McGraw-Hill.Google Scholar
Mitchell, T. M., Keller, R. M., and Kedar-Cabelli, S. T. 1986. Explanation-based generalization: A unifying view. Mach. Learn. 1, 1, 47–80.CrossRefGoogle Scholar
Redl, C. 2016. Automated benchmarking of KR-systems. In RCRA@AI*IA. CEUR Workshop Proceedings, vol. 1745. CEUR-WS.org, 45–56.Google Scholar
Russell, S. J. and Norvig, P. 2010. Artificial Intelligence – A Modern Approach, Third International Edition. Pearson Education.Google Scholar
Ryabokon, A. 2015. Knowledge-based (re)configuration of complex products and services. Ph.D. thesis, Alpen-Adria-Universität Klagenfurt.Google Scholar
Shepherdson, J. C. 1984. Negation as failure: A comparison of clark’s completed data base and reiter’s closed world assumption. J. Log. Program. 1, 1, 51–79.Google Scholar
Silva, J. P. M., Lynce, I., and Malik, S. 2009. Conflict-driven clause learning SAT solvers. In Handbook of Satisfiability. IOS Press, 131–153.Google Scholar
Taupe, R., Weinzierl, A., and Friedrich, G. 2019. Degrees of laziness in grounding – effects of lazy-grounding strategies on ASP solving. In LPNMR. LNCS, vol. 11481. Springer, 298–311.Google Scholar
Weinzierl, A. 2013. Learning non-ground rules for answer-set solving. In 2nd Workshop on Grounding and Transformations for Theories With Variables. 25–37.Google Scholar
Weinzierl, A. 2017. Blending lazy-grounding and CDNL search for answer-set solving. In LPNMR. LNCS, vol. 10377. Springer, 191–204.Google Scholar
Supplementary material: File

Taupe et al. supplementary material

Taupe et al. supplementary material

Download Taupe et al. supplementary material(File)
File 24.7 MB