2019/05/18 by Man Luo, Luo, Man · 1 citation
Computer Science · #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Logic, Reasoning, and Knowledge #Multi-Agent Systems and Negotiation #Natural Language Processing Techniques #cs.LO
paper · pdf · doi:10.48550/arxiv.1905.07550
arxiv created 2019/05/18 · openalex publication_date 2019/05/18 · arxiv updated 2019/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Strong equivalence is a well-studied and important concept in answer set programming (ASP). \rm LPMLN is a probabilistic extension of answer set programs with the weight scheme adapted from Markov Logic. Because of the semantic differences, strong equivalence for ASP does not simply carry over to \rm LPMLN. I study the concept of strong equivalence in \rm LPMLN with the goal of extending strong equivalence to \rm LPMLN programs. My study shows that the verification of strong equivalence in \rm LPMLN can be reduced to equivalence checking in classical logic plus weight consideration.The result allows us to leverage an answer set solver for checking strong equivalence in \rm LPMLN. Furthermore, this study also suggests us a few reformulations of the \rm LPMLN semantics using choice rules, logic of here and there, and the second-order logic. I will present my work result of strong equivalence for \rm LPMLN and talk about my next steps for research: one is approximately strong equivalence, and another is the integration of fuzzy logic with neural network.