Weight learning in a probabilistic extension of answer set programs

Joohyung Lee, Yi Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

LPMLN is a probabilistic extension of answer set programs with the weight scheme derived from that of Markov Logic. Previous work has shown how inference in LPMLN can be achieved. In this paper, we present the concept of weight learning in LPMLN and learning algorithms for LPMLN derived from those for Markov Logic. We also present a prototype implementation that uses answer set solvers for learning as well as some example domains that illustrate distinct features of LPMLN learning. Learning in LPMLN is in accordance with the stable model semantics, thereby it learns parameters for probabilistic extensions of knowledge-rich domains where answer set programming has shown to be useful but limited to the deterministic case, such as reachability analysis and reasoning about actions in dynamic domains. We also apply the method to learn the parameters for probabilistic abductive reasoning about actions.

Original languageEnglish (US)
Title of host publicationPrinciples of Knowledge Representation and Reasoning
Subtitle of host publicationProceedings of the 16th International Conference, KR 2018
EditorsMichael Thielscher, Francesca Toni, Frank Wolter
PublisherAAAI press
Pages22-31
Number of pages10
ISBN (Electronic)9781577358039
StatePublished - 2018
Event16th International Conference on the Principles of Knowledge Representation and Reasoning, KR 2018 - Tempe, United States
Duration: Oct 30 2018Nov 2 2018

Publication series

NamePrinciples of Knowledge Representation and Reasoning: Proceedings of the 16th International Conference, KR 2018

Conference

Conference16th International Conference on the Principles of Knowledge Representation and Reasoning, KR 2018
CountryUnited States
CityTempe
Period10/30/1811/2/18

ASJC Scopus subject areas

  • Software
  • Logic

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