Belief functions and default reasoning
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TLDR
In this article, a new approach to deal with default information based on the theory of belief functions is presented, inspired by Adams' epsilon semantics, where mass values are either close to 0 or close to 1.About:
This article is published in Artificial Intelligence.The article was published on 2000-09-01 and is currently open access. It has received 76 citations till now. The article focuses on the topics: Logical consequence.read more
Citations
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Environmental impact assessment using the evidential reasoning approach
TL;DR: The ER approach will be used to aggregate multiple environmental factors, resulting in an aggregated distributed assessment for each alternative policy, and a new analytical ER algorithm will be investigated which provides a means for using the ER approach in decision situations where an explicit ER aggregation function is needed.
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Analyzing the combination of conflicting belief functions
TL;DR: The nature of the combinations (conjunctive versus disjunctive, revision versus updating, static versus dynamic data fusion), argue about the need for a normalization, examine the possible origins of the conflicts, determine if a combination is justified and analyze many of the proposed solutions.
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The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties
TL;DR: In this article, a utility-based grade match method is proposed to transform both numerical data and qualitative (fuzzy) assessment information of various formats into the fuzzy belief structure, leading to a fuzzy belief decision matrix.
Decision Aiding The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties
TL;DR: Different from the existing ER algorithm that is of a recursive nature, the new fuzzy ER algorithm provides an analytical means for combining all attributes without iteration, thus providing scope and flexibility for sensitivity analysis and optimisation.
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Expressive probabilistic description logics
TL;DR: This paper presents sound and complete algorithms for the main reasoning problems in the new probabilistic description logics, which are based on reductions to reasoning in their classical counterparts, and to solving linear optimization problems.
References
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Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book
A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
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Fuzzy sets as a basis for a theory of possibility
TL;DR: The theory of possibility described in this paper is related to the theory of fuzzy sets by defining the concept of a possibility distribution as a fuzzy restriction which acts as an elastic constraint on the values that may be assigned to a variable.
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A logic for default reasoning
TL;DR: This paper proposes a logic for default reasoning, develops a complete proof theory and shows how to interface it with a top down resolution theorem prover, and provides criteria under which the revision of derived beliefs must be effected.