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Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More

Published online by Cambridge University Press:  21 September 2020

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

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Answer-Set Programming (ASP) is a powerful and expressive knowledge representation paradigm with a significant number of applications in logic-based AI. The traditional ground-and-solve approach, however, requires ASP programs to be grounded upfront and thus suffers from the so-called grounding bottleneck (i.e., ASP programs easily exhaust all available memory and thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been developed, but many state-of-the-art techniques for grounded ASP solving have not been available to them yet. In this work we present, for the first time, adaptions to the lazy-grounding setting for many important techniques, like restarts, phase saving, domain-independent heuristics, and learned-clause deletion. Furthermore, we investigate their effects and in general observe a large improvement in solving capabilities and also uncover negative effects in certain cases, indicating the need for portfolio solving as known from other solvers.

Keywords

Answer-Set ProgrammingLazy-GroundingSolving TechniquesKnowledge Representation

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. 609 - 624
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2020. Published by Cambridge University Press

References

Abels, D., Jordi, J., Ostrowski, M., Schaub, T., Toletti, A., and Wanko, P. 2019. Train scheduling with hybrid ASP. In LPNMR. Lecture Notes in Computer Science, vol. 11481. Springer, 3–17.Google Scholar
Alviano, M., Calimeri, F., Charwat, G., Dao-Tran, M., Dodaro, C., Ianni, G., Krennwallner, T., Kronegger, M., Oetsch, J., Pfandler, A., Pührer, J., Redl, C., Ricca, F., Schneider, P., Schwengerer, M., Spendier, L. K., Wallner, J. P., and Xiao, G. 2013. The fourth answer set programming competition: Preliminary report. In LPNMR. LNCS, vol. 8148. Springer, 42–53.Google Scholar
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. Lecture Notes in Computer Science, vol. 10377. Springer, 215–221.Google Scholar
Alviano, M., Dodaro, C., Faber, W., Leone, N., and Ricca, F. 2013. WASP: A native ASP solver based on constraint learning. In LPNMR. LNCS, vol. 8148. Springer, 54–66.Google Scholar
Anger, C., Gebser, M., Janhunen, T., and Schaub, T. 2006. What’s a head without a body? In ECAI. Frontiers in Artificial Intelligence and Applications, vol. 141. IOS Press, 769–770.Google Scholar
Aschinger, M., Drescher, C., Friedrich, G., Gottlob, G., Jeavons, P., Ryabokon, A., and Thorstensen, E. 2011. Optimization methods for the partner units problem. In CPAIOR. Lecture Notes in Computer Science, vol. 6697. Springer, 4–19.Google Scholar
Audemard, G. and Simon, L. 2009. Predicting learnt clauses quality in modern SAT solvers. In IJCAI. 399–404.Google Scholar
Audemard, G. and Simon, L. 2012. Refining restarts strategies for SAT and UNSAT. In CP. Lecture Notes in Computer Science, vol. 7514. Springer, 118–126.Google Scholar
Biere, A. and Fröhlich, A. 2018. Evaluating CDCL restart schemes. In POS@SAT. EPiC Series in Computing, vol. 59. EasyChair, 1–17.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
Bomanson, J., Janhunen, T., and Weinzierl, A. 2019. Enhancing lazy grounding with lazy normalization in answer-set programming. In AAAI. AAAI Press, 2694–2702.Google Scholar
Cabalar, P., Kaminski, R., Morkisch, P., and Schaub, T. 2019. telingo = ASP + time. In LPNMR. Lecture Notes in Computer Science, vol. 11481. Springer, 256–269.Google Scholar
Calimeri, F., Fuscà, D., Perri, S., and Zangari, J. 2017. I-DLV: the new intelligent grounder of DLV. Intelligenza Artificiale 11, 1, 5–20.Google Scholar
Calimeri, F., Gebser, M., Maratea, M., and Ricca, F. 2016. Design and results of the fifth answer set programming competition. Artif. Intell. 231, 151–181.CrossRefGoogle Scholar
Calimeri, F., Ianni, G., and Ricca, F. 2014. The third open answer set programming competition. Theory Pract. Log. Program. 14, 1, 117–135.Google Scholar
Clark, K. L. 1977. Negation as failure. In Logic and Data Bases. Advances in Data Base Theory. Plemum Press, New York, 293–322.Google Scholar
Cuteri, B., Dodaro, C., Ricca, F., and Schüller, P. 2019. Partial compilation of ASP programs. Theory Pract. Log. Program. 19, 5-6, 857–873.Google Scholar
Dao-Tran, M., Eiter, T., Fink, M., Weidinger, G., and Weinzierl, A. 2012. Omiga : An open minded grounding on-the-fly answer set solver. In JELIA. LNCS, vol. 7519. Springer, 480–483.Google Scholar
De Cat, B., Denecker, M., Bruynooghe, M., and Stuckey, P. J. 2015. Lazy model expansion: Interleaving grounding with search. J. Artif. Intell. Res. 52, 235–286.CrossRefGoogle Scholar
Eén, N. and Sörensson, N. 2003. An extensible sat-solver. In SAT. Lecture Notes in Computer Science, vol. 2919. Springer, 502–518.Google Scholar
Eiter, T., Kaminski, T., Redl, C., and Weinzierl, A. 2018. Exploiting partial assignments for efficient evaluation of answer set programs with external source access. J. Artif. Intell. Res. 62, 665–727.Google Scholar
Elffers, J., Giráldez-Cru, J., Gocht, S., Nordström, J., and Simon, L. 2018. Seeking practical CDCL insights from theoretical SAT benchmarks. In IJCAI. ijcai.org, 1300–1308.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
Falkner, A. A., Friedrich, G., Schekotihin, K., Taupe, R., and Teppan, E. C. 2018. Industrial applications of answer set programming. KI 32, 2-3, 165–176.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 Workshop Proceedings, vol. 755. CEUR-WS.org.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., and Wanko, P. 2016. Theory solving made easy with clingo 5. In ICLP (Technical Communications). OASICS, vol. 52. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2:1–2:15.Google Scholar
Gebser, M., Kaminski, R., König, A., and Schaub, T. 2011. Advances in gringo series 3. In LPNMR. LNCS, vol. 6645. Springer, 345–351.Google Scholar
Gebser, M., Kaufmann, B., Romero, J., Otero, R., Schaub, T., and Wanko, P. 2013. Domain-specific heuristics in answer set programming. In AAAI. AAAI Press.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., Sabuncu, O., and Schaub, T. 2011. An incremental answer set programming based system for finite model computation. AI Commun. 24, 2, 195–212.CrossRefGoogle Scholar
Goldberg, E. and Novikov, Y. 2002. Berkmin: A fast and robust SAT-solver. In Design, Automation and Test in Europe Conference and Exhibition, 2002. IEEE, 142–149.Google Scholar
Gomes, C. P., Selman, B., Crato, N., and Kautz, H. A. 2000. Heavy-tailed phenomena in satisfiability and constraint satisfaction problems. J. Autom. Reasoning 24, 1/2, 67–100.Google Scholar
Lefèvre, C., Béatrix, C., Stéphan, I., and Garcia, L. 2017. ASPeRiX, a first-order forward chaining approach for answer set computing. TPLP 17, 3, 266–310.Google Scholar
Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., and Scarcello, F. 2006. The DLV system for knowledge representation and reasoning. ACM Trans. Comput. Log. 7, 3, 499–562.Google Scholar
Leutgeb, L. and Weinzierl, A. 2017. Techniques for efficient lazy-grounding ASP solving. In DECLARE. Lecture Notes in Computer Science, vol. 10997. Springer, 132–148.Google Scholar
Luby, M., Sinclair, A., and Zuckerman, D. 1993. Optimal speedup of las vegas algorithms. Inf. Process. Lett. 47, 4, 173–180.Google Scholar
Marple, K., Bansal, A., Min, R., and Gupta, G. 2012. Goal-directed execution of answer set programs. In PPDP. ACM, 35–44.Google Scholar
Marple, K., Salazar, E., and Gupta, G. 2017. Computing stable models of normal logic programs without grounding. CoRR abs/1709.00501.Google Scholar
Moskewicz, M. W., Madigan, C. F., Zhao, Y., Zhang, L., and Malik, S. 2001. Chaff: Engineering an efficient SAT solver. In DAC. ACM, 530–535.Google Scholar
Palù, A. D., Dovier, A., Pontelli, E., and Rossi, G. 2009. GASP: answer set programming with lazy grounding. Fundam. Inform. 96, 3, 297–322.Google Scholar
Pipatsrisawat, K. and Darwiche, A. 2007. A lightweight component caching scheme for satisfiability solvers. In SAT. Lecture Notes in Computer Science, vol. 4501. Springer, 294–299.Google Scholar
Pretolani, D. 1993. Efficiency, and stability of hypergraph SAT algorithms. In Cliques, Coloring, and Satisfiability. Vol. 26. DIMACS/AMS, 479–498.Google 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
Taupe, R., Schekotihin, K., Schüller, P., Weinzierl, A., and Friedrich, G. 2019. Exploiting partial knowledge in declarative domain-specific heuristics for ASP. In ICLP Technical Communications. EPTCS, vol. 306. 22–35.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. Lecture Notes in Computer Science, vol. 11481. Springer, 298–311.Google Scholar
Taupe, R., Weinzierl, A., and Schenner, G. 2017. Introducing heuristics for lazy-grounding ASP solving. In 1st International Workshop on Practical Aspects of Answer Set Programming.Google Scholar
Teppan, E. C. 2017. On the complexity of the partner units decision problem. Artif. Intell. 248, 112–122.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