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By Finn Verner Jensen (auth.), Salem Benferhat, Philippe Besnard (eds.)
This e-book constitutes the refereed court cases of the sixth ecu convention on Symbolic and Quantitative ways to Reasoning with Uncertainty, ECSQARU 2001, held in Toulouse, France in September 2001.
The sixty eight revised complete papers offered including 3 invited papers have been conscientiously reviewed and chosen from over 100 submissions. The e-book deals topical sections on selection concept, in part observable Markov determination techniques, decision-making, coherent percentages, Bayesian networks, studying causal networks, graphical illustration of uncertainty, obscure possibilities, trust features, fuzzy units and tough units, threat concept, merging, trust revision and personal tastes, inconsistency dealing with, default good judgment, common sense programming, etc.
Read Online or Download Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 6th European Conference, ECSQARU 2001 Toulouse, France, September 19–21, 2001 Proceedings PDF
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Additional info for Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 6th European Conference, ECSQARU 2001 Toulouse, France, September 19–21, 2001 Proceedings
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Section 3 presents an illustrative example and section 4 includes conclusions. 2 Extension of the Rough Set Concept on Multicriteria Decision Problems In any multicriteria and/or multiattribute decision problem, no recommendation can be elaborated before the DM provides some preferential information suitable to the preference model assumed [16,23]. There are two major models used until now in multicriteria decision analysis: functional and relational. The functional model has been extensively used within the framework of multiattribute utility theory [15].
On the other hand, MCDA deals with problems where descriptions (evaluations) of actions by means of criteria, as well as decisions in sorting, choice and ranking problems, are preference-ordered. The extension of the rough set approach to problems in which preference-order properties are meaningful is possible upon two important methodological contributions extensivelly discussed in this paper: 1) approximation of the comprehensive preference relation by dominance relations, which allows to deal with preference-order properties of criteria, 2) analysis of decision examples in a pairwise comparison table, which allows to represent preference relations for choice and ranking problems.
Considering two intervals [a, b], [c, d] (possibly reduced to singletons) of E, we say that [a, b] is to the left (resp. to the right) of [c, d] if b < c (resp. a > d). This is denoted as [a, b] [c, d] (resp. [a, b] [c, d]). Lastly, for any interval I = [a, b], we denote by [a, b] or |I| the number of elements in interval [a, b], and denote the bounds by I := a, and I := b. e. the interval [mini ai , maxi ai ]) by a := min ai , and a := max ai . By simplicity, the support is denoted a . We state more precisely our problem using the following definition.