Automated Aggregator - Rewriting with the Counting Aggregate
| Autor: | Michael Dingess, Miroslaw Truszczynski |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
I.2.2 Computer Science - Logic in Computer Science I.2.3 Selection (relational algebra) Computer Science - Artificial Intelligence Computer science Programming language Process (engineering) 02 engineering and technology Solver computer.software_genre Logic in Computer Science (cs.LO) 020202 computer hardware & architecture News aggregator Precondition Answer set programming Artificial Intelligence (cs.AI) F.4.1 0202 electrical engineering electronic engineering information engineering Constraint programming 020201 artificial intelligence & image processing Rewriting computer |
| Zdroj: | Electronic Proceedings in Theoretical Computer Science. 325:96-109 |
| ISSN: | 2075-2180 |
| DOI: | 10.4204/eptcs.325.17 |
| Popis: | 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. In Proceedings ICLP 2020, arXiv:2009.09158 |
| Databáze: | OpenAIRE |
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