Ordinal and probabilistic representations of acceptance

Ordinal and probabilistic representations of acceptance (2004)

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by Didier Dubois , Helene Fargier , Henri Prade

Venue:	J. Artificial Intelligence Research
Citations:	6 - 0 self

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Datum	Value	Source
TITLE	Ordinal and probabilistic representations of acceptance	INFERENCE
AUTHOR NAME	Didier Dubois	SVM HeaderParse 0.2
AUTHOR NAME	Helene Fargier	SVM HeaderParse 0.2
AUTHOR NAME	Henri Prade	SVM HeaderParse 0.2
AUTHOR AFFIL	Institut de Recherche en Informatique de Toulouse; Université Paul Sabatier � 118 route de Narbonne	SVM HeaderParse 0.2
AUTHOR ADDR	31062 Toulouse Cedex 4 � France	SVM HeaderParse 0.2
ABSTRACT	An accepted belief is a proposition considered likely enough by an agent, to be inferred from as if it were true. This paper bridges the gap between probabilistic and logical representations of accepted beliefs. To this end, natural properties of relations on propositions, describing relative strength of belief are augmented with some conditions ensuring that accepted beliefs form a deductively closed set. This requirement turns out to be very restrictive. In particular, it is shown that the sets of accepted belief of an agent can always be derived from a family of possibility rankings of states. An agent accepts a proposition in a given context if this proposition is considered more possible than its negation in this context, for all possibility rankings in the family. These results are closely connected to the non-monotonic 'preferential' inference system of Kraus, Lehmann and Magidor and the so-called plausibility functions of Friedman and Halpern. The extent to which probability theory is compatible with acceptance relations is laid bare. A solution to the lottery paradox, which is considered as a major impediment to the use of non-monotonic inference is proposed using a special kind of probabilities (called lexicographic, or big-stepped). The setting of acceptance relations also proposes another way of approaching the theory of belief change after the works of Gärdenfors and colleagues. Our view considers the acceptance relation as a primitive object from which belief sets are derived in various contexts. 1.	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
VENUE	J. Artificial Intelligence Research	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	23--56	INFERENCE
VOLUME	2004	INFERENCE
CITATIONS	36 found	ParsCit 1.0