Probabilistic belief contraction using argumentation

Kinzang Chhogyal, Abhaya Nayak, Zhiqiang Zhuang, Abdul Sattar

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)


When a belief state is represented as a probability function P, the resulting belief state of the contraction of a sentence (belief) from the original belief state P can be given by the probabilistic version of the Harper Identity. Specifically, the result of contracting P by a sentence h is taken to be the mixture of two states: the original state P, and the resultant state P h of revising P by the negation of h. What proportion of P and P h should be used in this mixture remains an open issue and is largely ignored in literature. In this paper, we first classify different belief states by their stability, and then exploit the quantitative nature of probabilities and combine it with the basic ideas of argumentation theory to determine the mixture proportions. We, therefore, propose a novel approach to probabilistic belief contraction using argumentation.

Original languageEnglish
Title of host publicationProceedings of the 24th International Joint Conference on Artificial Intelligence, IJCAI 2015
EditorsQiang Yang, Michael Wooldridge
Place of PublicationPalo Alto, CA
PublisherAssociation for the Advancement of Artificial Intelligence
Number of pages7
ISBN (Electronic)9781577357384
Publication statusPublished - 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: 25 Jul 201531 Jul 2015

Publication series

NameInternational Joint Conference on Artificial Intelligence Proceedings
PublisherAAAI Press
ISSN (Print)1045-0823


Other24th International Joint Conference on Artificial Intelligence, IJCAI 2015
CityBuenos Aires

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