Abstract
Belief change is concerned with modelling the way in which an idealised (rational) reasoner maintains their beliefs and the way in which those beliefs are modified as the reasoner acquires new information. The AGM [1,3,5] framework is the most widely cited belief change methodology in the literature. It models the reasoner’s belief state as a set of sentences that is logically closed under deduction and provides for three belief change operations: expansion, contraction and revision. Each of the AGM belief change operations is motivated by principles of rationality that are formalised by way of rationality postulates.
Pagnucco [10] formalised a way of implementing the AGM belief change operations using a knowledge compilation technique involving prime implicates in order to improve computational efficiency. This technique exploits the epistemic entrenchment construction for AGM belief change by Gärdenfors and Makinson [4] by introducing the notion of a compiled epistemic entrenchment. It has a number of significant features: (a) the belief change operators constructed satisfy the AGM postulates; (b) when compilation has been effected only subsumption checking and some simple syntactic manipulation is required in order to contract (or revise) the reasoner’s belief state.
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Zhuang, Z.Q., Pagnucco, M., Meyer, T. (2007). Implementing Iterated Belief Change Via Prime Implicates. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_52
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