Rational Partial Choice Functions and Their Application to Belief Revision.

Jianbing Ma, Weiru Liu, Didier Dubois

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Necessary and sufficient conditions for choice functions to be rational have been intensively studied in the past. However, in these attempts, a choice function is completely specified. That is, given any subset of options, called an issue, the best option over that issue is always known, whilst in real-world scenarios, it is very often that only a few choices are known instead of all. In this paper, we study partial choice functions and investigate necessary and sufficient rationality conditions for situations where only a few choices are known. We prove that our necessary and sufficient condition for partial choice functions boils down to the necessary and sufficient conditions for complete choice functions proposed in the literature. Choice functions have been instrumental in belief revision theory. That is, in most approaches to belief revision, the problem studied can simply be described as the choice of possible worlds compatible with the input information, given an agent’s prior belief state. The main effort has been to devise strategies in order to infer the agents revised belief state. Our study considers the converse problem: given a collection of input information items and their corresponding revision results (as provided by an agent), does there exist a rational revision operation used by the agent and a consistent belief state that may explain the observed results?
Original languageEnglish
Title of host publicationKnowledge Science, Engineering and Management
Subtitle of host publication8th International Conference, KSEM 2015, Chongqing, China, October 28-30, 2015, Proceedings
Number of pages13
ISBN (Electronic)9783319251592
ISBN (Print)9783319251585
Publication statusPublished - 3 Nov 2015

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


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