Encoding probabilistic causal model in probabilistic action language

Nam Tran, Chitta Baral

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

5 Scopus citations

Abstract

Pearl's probabilistic causal model has been used in many domains to reason about causality. Pearl's treatment of actions is very different from the way actions are represented explicitly in action languages. In this paper we show how to encode Pearl's probabilistic causal model in the action language PAL thus relating this two distinct approaches to reasoning about actions.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Pages305-310
Number of pages6
StatePublished - 2004
EventProceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) - San Jose, CA, United States
Duration: Jul 25 2004Jul 29 2004

Other

OtherProceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004)
CountryUnited States
CitySan Jose, CA
Period7/25/047/29/04

ASJC Scopus subject areas

  • Software

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  • Cite this

    Tran, N., & Baral, C. (2004). Encoding probabilistic causal model in probabilistic action language. In Proceedings of the National Conference on Artificial Intelligence (pp. 305-310)