Computer Science – Artificial Intelligence
Scientific paper
1998-08-29
Computer Science
Artificial Intelligence
This is an expanded version of a paper that appeared in AAAI '96
Scientific paper
We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. We focus on one application of plausibility measures in this paper: default reasoning. In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, $\epsilon$-semantics, possibilistic structures, and $\kappa$-rankings, that have been shown to be characterized by the same set of axioms, known as the KLM properties. While this was viewed as a surprise, we show here that it is almost inevitable. In the framework of plausibility measures, we can give a necessary condition for the KLM axioms to be sound, and an additional condition necessary and sufficient to ensure that the KLM axioms are complete. This additional condition is so weak that it is almost always met whenever the axioms are sound. In particular, it is easily seen to hold for all the proposals made in the literature.
Friedman Nir
Halpern Joseph Y.
No associations
LandOfFree
Plausibility Measures and Default Reasoning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Plausibility Measures and Default Reasoning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Plausibility Measures and Default Reasoning will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-411715