Modeling Belief in Dynamic Systems, Part II: Revision and Update

TL;DR

This paper presents a unified framework combining temporal, epistemic, and plausibility aspects to model belief revision and update, clarifying their differences and underlying principles in AI.

Contribution

It introduces a new framework for modeling belief change that unifies revision and update, and analyzes their assumptions and minimal change principles.

Findings

Belief revision and update can be captured within the new framework.

Katsuno and Mendelzon's belief update depends on strong, limiting assumptions.

A notion of minimal change underlies various belief change operations.

Abstract

The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper (Friedman & Halpern, 1997), we introduce a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the change of beliefs over time. In this paper, we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method, and to better understand the principles underlying them. In particular, it shows that Katsuno and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on several strong assumptions that may limit its applicability in artificial intelligence. Finally, our analysis allow us to identify a notion of minimal change that underlies a broad range of belief change operations including revision and update.