Uncertainty in Artificial Intelligence
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Non-monotonic Reasoning and the Reversibility of Belief Change
Daniel Hunter
Traditional approaches to non-monotonic reasoning fail to satisfy a number of plausible axioms for belief revision and suffer from conceptual difficulties as well. Recent work on ranked preferential models (RPMs) promises to overcome some of these difficulties. Here we show that RPMs are not adequate to handle iterated belief change. Specifically, we show that RPMs do not always allow for the reversibility of belief change. This result indicates the need for numerical strengths of belief.
Pages: 159-164
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PDF Link: /papers/91/p159-hunter.pdf
AUTHOR = "Daniel Hunter ",
TITLE = "Non-monotonic Reasoning and the Reversibility of Belief Change",
BOOKTITLE = "Proceedings of the Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-91)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1991",
PAGES = "159--164"

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