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Strong Equivalence of Logic Programs with Counting

Published online by Cambridge University Press:  24 June 2022

VLADIMIR LIFSCHITZ*
Affiliation:
University of Texas at Austin, USA (e-mail: [email protected])

Abstract

In answer set programming, two groups of rules are considered strongly equivalent if they have the same meaning in any context. In some cases, strong equivalence of programs in the input language of the grounder gringo can be established by deriving rules of each program from rules of the other. The possibility of such proofs has been demonstrated for a subset of that language that includes comparisons, arithmetic operations, and simple choice rules, but not aggregates. This method is extended here to a class of programs in which some uses of the #count aggregate are allowed.

Type
Original Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Faber, W., Pfeifer, G. and Leone, N. 2011. Semantics and complexity of recursive aggregates in answer set programming. Artificial Intelligence 175, 278298.CrossRefGoogle Scholar
Fandinno, J., Nansen, Z. and Lierler, Y. 2022. Axiomatization of aggregates in answer set programming. In Proceedings of the AAAI Conference on Artificial Intelligence. To appear.CrossRefGoogle Scholar
Gebser, M., Harrison, A., Kaminski, R., Lifschitz, V. and Schaub, T. 2015. Abstract Gringo. Theory and Practice of Logic Programming 15, 449463.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Lindauer, M., Ostrowski, M., Romero, J., Schaub, T. and Thiele, S. 2019. Potassco User Guide. URL: https://github.com/potassco/guide/releases/ Google Scholar
Gelfond, M. and Kahl, Y. 2014. Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach. Cambridge University Press.CrossRefGoogle Scholar
Gelfond, M. and Zhang, Y. 2019. Vicious circle principle, aggregates, and formation of sets in ASP based languages. Artificial Intelligence 275, 2877.CrossRefGoogle Scholar
Harrison, A., Lifschitz, V., Pearce, D. and Valverde, A. 2017. Infinitary equilibrium logic and strongly equivalent logic programs. Artificial Intelligence 246, 2233.CrossRefGoogle Scholar
Lifschitz, V. 2019. Answer Set Programming. Springer.CrossRefGoogle Scholar
Lifschitz, V. 2021. Here and there with arithmetic. Theory and Practice of Logic Programming.CrossRefGoogle Scholar
Lifschitz, V., Lühne, P. and Schaub, T. 2019. Verifying strong equivalence of programs in the input language of gringo. In Proceedings of the 15th International Conference on Logic Programming and Non-monotonic Reasoning.CrossRefGoogle Scholar
Lifschitz, V., Pearce, D. and Valverde, A. 2001. Strongly equivalent logic programs. ACM Transactions on Computational Logic 2, 526541.CrossRefGoogle Scholar
Marek, V. and Truszczynski, M. 1999. Stable models and an alternative logic programming paradigm. In The Logic Programming Paradigm: A 25-Year Perspective. Springer Verlag, 375398.CrossRefGoogle Scholar
Niemelä, I. 1999. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 241273.CrossRefGoogle Scholar
Truszczynski, M. 2012. Connecting first-order ASP and the logic FO(ID) through reducts. In Correct Reasoning: Essays on Logic-Based AI in Honor of Vladimir Lifschitz, E. Erdem, J. Lee, Y. Lierler and D. Pearce, Eds. Springer, 543559.CrossRefGoogle Scholar