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

Published online by Cambridge University Press:  21 September 2020

RICHARD TAUPE Open the ORCID record for RICHARD TAUPE [Opens in a new window] ,
ANTONIUS WEINZIERL Open the ORCID record for ANTONIUS WEINZIERL [Opens in a new window]  and
GERHARD FRIEDRICH
RICHARD TAUPE
Affiliation:
Siemens AG Österreich, (e-mail: [email protected]) Alpen-Adria-Universität, Klagenfurt, Austria, (e-mail: [email protected])
ANTONIUS WEINZIERL
Affiliation:
TU Wien (Vienna University of Technology), Austria, (e-mail: [email protected])
GERHARD FRIEDRICH
Affiliation:
Alpen-Adria-Universität, Klagenfurt, Austria, (e-mail: [email protected])
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Abstract

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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
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
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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

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