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Translating P-log, LPMLN, LPOD, and CR-Prolog2 into Standard Answer Set Programs

Author Zhun Yang

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Zhun Yang
  • School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Arizona State University, P.O. Box 878809, Tempe, AZ 85287, United States

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Zhun Yang. Translating P-log, LPMLN, LPOD, and CR-Prolog2 into Standard Answer Set Programs. In Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018). Open Access Series in Informatics (OASIcs), Volume 64, pp. 17:1-17:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/OASIcs.ICLP.2018.17

Abstract

Answer set programming (ASP) is a particularly useful approach for nonmonotonic reasoning in knowledge representation. In order to handle quantitative and qualitative reasoning, a number of different extensions of ASP have been invented, such as quantitative extensions LP^{MLN} and P-log, and qualitative extensions LPOD, and CR-Prolog_2.
Although each of these formalisms introduced some new and unique concepts, we present reductions of each of these languages into the standard ASP language, which not only gives us an alternative insight into the semantics of these extensions in terms of the standard ASP language, but also shows that the standard ASP is capable of representing quantitative uncertainty and qualitative uncertainty. What's more, our translations yield a way to tune the semantics of LPOD and CR-Prolog_2. Since the semantics of each formalism is represented in ASP rules, we can modify their semantics by modifying the corresponding ASP rules.
For future work, we plan to create a new formalism that is capable of representing quantitative and qualitative uncertainty at the same time. Since LPOD rules are simple and informative, we will first try to include quantitative preference into LPOD by adding the concept of weight and tune the semantics of LPOD by modifying the translated standard ASP rules.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Knowledge representation and reasoning
Keywords
  • answer set programming
  • preference
  • LPOD
  • CR-Prolog

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