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  • 标题:Efficient HEX-Program Evaluation Based on Unfounded Sets
  • 本地全文:下载
  • 作者:T. Eiter ; M. Fink ; T. Krennwallner
  • 期刊名称:Journal of Artificial Intelligence Research
  • 印刷版ISSN:1076-9757
  • 出版年度:2014
  • 卷号:49
  • 页码:269-321
  • 出版社:American Association of Artificial
  • 摘要:HEX-programs extend logic programs under the answer set semantics with external computations through external atoms. As reasoning from ground Horn programs with nonmonotonic external atoms of polynomial complexity is already on the second level of the polynomial hierarchy, minimality checking of answer set candidates needs special attention. To this end, we present an approach based on unfounded sets as a generalization of related techniques for ASP programs. The unfounded set detection is expressed as a propositional SAT problem, for which we provide two different encodings and optimizations to them. We then integrate our approach into a previously developed evaluation framework for HEX-programs, which is enriched by additional learning techniques that aim at avoiding the reconstruction of the same or related unfounded sets. Furthermore, we provide a syntactic criterion that allows one to skip the minimality check in many cases. An experimental evaluation shows that the new approach significantly decreases runtime.
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