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A framework for belief revision under restrictions

Published online by Cambridge University Press:  12 September 2022

Zhiguo Long Open the ORCID record for Zhiguo Long [Opens in a new window] ,
Hua Meng Open the ORCID record for Hua Meng [Opens in a new window] ,
Tianrui Li ,
Heng-Chao Li  and
Michael Sioutis
Zhiguo Long
Affiliation:
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China
Hua Meng
Affiliation:
School of Mathematics, Southwest Jiaotong University, Chengdu, China E-mail: [email protected]
Tianrui Li
Affiliation:
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China
Heng-Chao Li
Affiliation:
School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China
Michael Sioutis
Affiliation:
Faculty of Information Systems and Applied Computer Sciences, University of Bamberg, Bamberg, Germany
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Abstract

Traditional belief revision usually considers generic logic formulas, whilst in practical applications some formulas might even be inappropriate for beliefs. For instance, the formula $p \wedge q$ is syntactically consistent and is also an acceptable belief when there are no restrictions, but it might become unacceptable under restrictions in some context. If we assume that p represents ‘manufacturing product A’ and q represents ‘manufacturing product B’, an example of such a context would be the knowledge that there are not enough resources to manufacture them both and, hence, $p \wedge q$ would not be an acceptable belief. In this article, we propose a generic framework for belief revision under restrictions. We consider restrictions of either fixed or dynamic nature, and devise several postulates to characterize the behaviour of changing beliefs when new evidence emerges or the restriction changes. Moreover, we show that there is a representation theorem for each type of restriction. Finally, we discuss belief revision of qualitative spatio-temporal information under restrictions as an application of this new framework.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 37 , 2022 , e8
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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