Abstract
Current belief change literature is largely confined to atemporal belief change – the temporal element of beliefs is not explicitly recognized or represented. In this paper, we present a temporal belief change framework that is based on applying Spohn’s theory of ranking functions to certain temporal semantic objects that we call ’histories’. The resulting framework allows us to address a class of problems for which Jeffery’s general conditionalization, and Spohn’s cardinality of the ranks, as well as the dependencies between beliefs play a central role. This allows us to lend further support to the argument that the application of the AGM theory is not necessarily limited to a static world. We also present an interpretation of belief update in the context of ranking-functions that has been missing in the literature.
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Hezart, A., Nayak, A., Orgun, M.A. (2010). Temporal Belief-Change: κ-functions Approach. In: Li, J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science(), vol 6464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17432-2_2
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DOI: https://doi.org/10.1007/978-3-642-17432-2_2
Publisher Name: Springer, Berlin, Heidelberg
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