Reasoning about actions in a probabilistic setting

Chitta Baral, Nam Tran, Le Chi Tuan

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

26 Scopus citations

Abstract

In this paper we present a language to reason about actions in a probabilistic setting and compare our work with earlier work by Pearl. The main feature of our language is its use of static and dynamic causal laws, and use of unknown (or background) variables - whose values are determined by factors beyond our model - in incorporating probabilities. We use two kind of unknown variables: inertial and non-inertial. Inertial unknown variables are helpful in assimilating observations and modeling counterfactuals and causality; while non-inertial unknown variables help characterize stochastic behavior, such as the outcome of tossing a coin, that are not impacted by observations. Finally, we give a glimpse of incorporating probabilities into reasoning with narratives.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Pages507-512
Number of pages6
StatePublished - 2002
Event18th National Conference on Artificial Intelligence (AAAI-02), 14th Innovative Applications of Artificial Intelligence Conference (IAAI-02) - Edmonton, Alta., Canada
Duration: Jul 28 2002Aug 1 2002

Other

Other18th National Conference on Artificial Intelligence (AAAI-02), 14th Innovative Applications of Artificial Intelligence Conference (IAAI-02)
CountryCanada
CityEdmonton, Alta.
Period7/28/028/1/02

ASJC Scopus subject areas

  • Software

Cite this

Baral, C., Tran, N., & Tuan, L. C. (2002). Reasoning about actions in a probabilistic setting. In Proceedings of the National Conference on Artificial Intelligence (pp. 507-512)