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Plausibility Measures: A User's Guide
 In Proc. Eleventh Conference on Uncertainty in Artificial Intelligence (UAI '95
, 1995
"... We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered set. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probab ..."
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Cited by 30 (7 self)
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We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered set. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. The lack of structure in a plausibility measure makes it easy for us to add structure on an "as needed" basis, letting us examine what is required to ensure that a plausibility measure has certain properties of interest. This gives us insight into the essential features of the properties in question, while allowing us to prove general results that apply to many approaches to reasoning about uncertainty. Plausibility measures have already proved useful in analyzing default reasoning. In this paper, we examine their "algebraic properties", analogues to the use of + and \Theta in probability theory. An understanding of such properties ...
Defining Explanation in Probabilistic Systems
 In Proc. UAI97
, 1997
"... As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to ..."
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Cited by 28 (3 self)
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As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to explanation in the literature one due to G ardenfors and one due to Pearland show that both suffer from significant problems. We propose an approach to defining a notion of "better explanation" that combines some of the features of both together with more recent work by Pearl and others on causality. 1 INTRODUCTION Probabilistic inference is often hard for humans to understand. Even a simple inference in a small domain may seem counterintuitive and surprising; the situation only gets worse for large and complex domains. Thus, a system doing probabilistic inference must be able to explain its findings and recommendations to evoke confidence on the part of the user. Indeed, in experiments wi...
Conditional Logics of Belief Change
 In Proc. National Conference on Artificial Intelligence (AAAI '94
, 1994
"... The study of belief changehas been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. Belief revision and update are clearly not the only possible notions of belief change. In this paper we investi ..."
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Cited by 13 (4 self)
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The study of belief changehas been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. Belief revision and update are clearly not the only possible notions of belief change. In this paper we investigate properties of a range of possible belief change operations. We start with an abstract notion of a belief change system and provide a logical language that describes belief change in such systems. We then consider several reasonable properties one can impose on such systems and characterize them axiomatically. We show that both belief revision and update fit into our classification. As a consequence, we get both a semantic and an axiomatic (prooftheoretic) characterization of belief revision and update (as well as some belief change operations that generalize them), in one natural framework. Introduction The study of belief change has been an active area in philosophy and in artificial...
A Qualitative Markov Assumption and Its Implications for Belief Change
 In Proc. Twelfth Conference on Uncertainty in Artificial Intelligence (UAI '96
, 1996
"... The study of belief change has been an active area in philosophyand AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly speaking, revision treats a surprising observation as a sign that previous beliefs were wrong, while up ..."
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Cited by 13 (8 self)
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The study of belief change has been an active area in philosophyand AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly speaking, revision treats a surprising observation as a sign that previous beliefs were wrong, while update treats a surprising observation as an indication that the world has changed. In general, we would expect that an agent making an observation may both want to revise some earlier beliefs and assume that some change has occurred in the world. We define a novel approach to belief change that allows us to do this, by applying ideas from probability theory in a qualitative settings. The key idea is to use a qualitative Markov assumption, which says that state transitions are independent. We show that a recent approach to modeling qualitative uncertainty using plausibility measures allows us to make such a qualitative Markov assumption in a relatively straightforward way, and show how the Ma...
Plausibility Measures and Default Reasoning: An Overview
 Proceedings, 14th Symposium on Logic in Computer Science
, 1999
"... 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 then consider one application of plausibility measures ..."
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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 then consider one application of plausibility measures: default reasoning. In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, fflsemantics, possibilistic structures, and 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 ...