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 Ag- gregator 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.
|Number of pages||14|
|Journal||Electronic Proceedings in Theoretical Computer Science, EPTCS|
|State||Published - Sep 19 2020|
|Event||36th International Conference on Logic Programming, ICLP 2020 - Virtual, Rende, Italy|
Duration: Sep 18 2020 → Sep 24 2020
Bibliographical noteFunding Information:
∗Research supported by National Science Foundation grant 1707371
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