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Computer Science > Artificial Intelligence

arXiv:2009.10240v1 (cs)
[Submitted on 22 Sep 2020]

Title:Automated Aggregator -- Rewriting with the Counting Aggregate

Authors:Michael Dingess (University of Kentucky), Miroslaw Truszczynski (University of Kentucky)
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Abstract:Answer set programming is a leading declarative constraint programming paradigm with wide use for complex knowledge-intensive applications. Modern answer set programming languages support many equivalent ways to model constraints and specifications in a program. However, so far answer set programming has failed to develop systematic methodologies for building representations that would uniformly lend well to automated processing. This suggests that encoding selection, in the same way as algorithm selection and portfolio solving, may be a viable direction for improving performance of answer-set solving. The necessary precondition is automating the process of generating possible alternative encodings. Here we present an automated rewriting system, the Automated Aggregator or AAgg, that given a non-ground logic program, produces a family of equivalent programs with complementary performance when run under modern answer set programming solvers. We demonstrate this behavior through experimental analysis and propose the system's use in automated answer set programming solver selection tools.
Comments: In Proceedings ICLP 2020, arXiv:2009.09158
Subjects: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
ACM classes: F.4.1;I.2.2;I.2.3
Cite as: arXiv:2009.10240 [cs.AI]
  (or arXiv:2009.10240v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2009.10240
Journal reference: EPTCS 325, 2020, pp. 96-109
Related DOI: https://doi.org/10.4204/EPTCS.325.17

Submission history

From: EPTCS [view email] [via EPTCS proxy]
[v1] Tue, 22 Sep 2020 00:48:33 UTC (20 KB)
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