FLP Semantics Without Circular Justifications for General Logic Programs

Authors

  • Yi-Dong Shen Chinese Academy of Sciences
  • Kewen Wang Griffith University

DOI:

https://doi.org/10.1609/aaai.v26i1.8211

Keywords:

answer set programming, semantics

Abstract

The FLP semantics presented by (Faber, Leone, and Pfeifer 2004) has been widely used to define answer sets, called FLP answer sets, for different types of logic programs such as logic programs with aggregates, description logic programs (dl-programs), Hex programs, and logic programs with first-order formulas (general logic programs). However, it was recently observed that the FLP semantics may produce unintuitive answer sets with circular justifications caused by self-supporting loops. In this paper, we address the circular justification problem for general logic programs by enhancing the FLP semantics with a level mapping formalism. In particular, we extend the Gelfond-Lifschitz three step definition of the standard answer set semantics from normal logic programs to general logic programs and define for general logic programs the first FLP semantics that is free of circular justifications. We call this FLP semantics the well-justified FLP semantics. This method naturally extends to general logic programs with additional constraints like aggregates, thus providing a unifying framework for defining the well-justified FLP semantics for various types of logic programs. When this method is applied to normal logic programs with aggregates, the well-justified FLP semantics agrees with the conditional satisfaction based semantics defined by (Son, Pontelli, and Tu 2007); and when applied to dl-programs, the semantics agrees with the strongly well-supported semantics defined by (Shen 2011).

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Published

2021-09-20

How to Cite

Shen, Y.-D., & Wang, K. (2021). FLP Semantics Without Circular Justifications for General Logic Programs. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 821-827. https://doi.org/10.1609/aaai.v26i1.8211

Issue

Section

AAAI Technical Track: Knowledge Representation and Reasoning