Using the probabilistic logic programming language P-log for causal and counterfactual reasoning and non-naive conditioning

Chitta Baral, Matt Hunsaker

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

30 Scopus citations

Abstract

P-log is a probabilistic logic programming language, which combines both logic programming style knowledge representation and probabilistic reasoning. In earlier papers various advantages of P-log have been discussed. In this paper we further elaborate on the KR prowess of P-log by showing that: (i) it can be used for causal and counterfactual reasoning and (ii) it provides an elaboration tolerant way for non-naive conditioning.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Pages243-249
Number of pages7
StatePublished - 2007
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: Jan 6 2007Jan 12 2007

Other

Other20th International Joint Conference on Artificial Intelligence, IJCAI 2007
CountryIndia
CityHyderabad
Period1/6/071/12/07

ASJC Scopus subject areas

  • Artificial Intelligence

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    Baral, C., & Hunsaker, M. (2007). Using the probabilistic logic programming language P-log for causal and counterfactual reasoning and non-naive conditioning. In IJCAI International Joint Conference on Artificial Intelligence (pp. 243-249)