Probabilistic Belief Revision via Similarity of Worlds

Rens, Gavin and Meyer, Thomas and Kern-Isberner, Gabriele and Nayak, Abhaya (2018) Probabilistic Belief Revision via Similarity of Worlds, Proceedings of KI 2018: Advances in Artificial Intelligence 41st German Conference on AI, September 24–28, 2018, Berlin, Germany, 11117, 343-356, Springer.

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Similarity among worlds plays a pivotal role in providing the semantics for different kinds of belief change. Although similarity is, in- tuitively, a context-sensitive concept, the accounts of similarity presently proposed are, by and large, context blind. We propose an account of sim- ilarity that is context sensitive, and when belief change is concerned, we take it that the evidence (epistemic input) provides the required context. We accordingly develop and examine two accounts of probabilistic belief change that are based on such evidence-sensitive similarity. The first of these switches between two extreme behaviors depending on whether or not the evidence in question is consistent with the current knowledge. The second one gracefully changes its behavior depending on the degree to which the evidence is consistent with current knowledge. Finally, we analyze these two belief change operators with respect to a select set of plausible postulates.

Item Type: Conference paper
Uncontrolled Keywords: Belief revision Probability Similarity Bayesian conditioning Lewis imaging
Subjects: Computing methodologies > Artificial intelligence
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Date Deposited: 10 Jan 2019
Last Modified: 10 Oct 2019 15:31

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