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Using the transferable belief model and a qualitative possibility theory approach on an illustrative example: The assessment of the value of a candidate. (English) Zbl 1005.68148
Summary: The problem of assessing the value of a candidate is viewed here as a multiple combination problem. On the one hand, a candidate can be evaluated according to different criteria, and on the other hand, several experts are supposed to assess the value of candidates according to each criterion. Criteria are not equally important, experts are not equally competent or reliable. Moreover, levels of satisfaction of criteria, or levels of confidence are only assumed to take their values in linearly ordered scales, whose nature is rather qualitative. The problem is discussed within two frameworks, the transferable belief model and the qualitative possibility theory. They respectively offer a quantitative and a qualitative setting for handling the problem, thus providing a way to emphasize what are the underlying assumptions in each approach.

68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
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[1] From semantic to syntactic approaches to information combination in possibilistic logic. In: editor. Aggregation and fusion of imperfect information. Heidelberg: Physica-Verlag; 1997, p 141-161.
[2] Biswas, Fuzzy Sets Syst 74 pp 187– (1995) · Zbl 0865.92031
[3] A theoretical framework for measuring attractiveness by a categorical based evaluation technique (MACBETH). In: Proceedings of the XIth Int. Conf. on MCDM, Coimbra, Portugal, 1994, p 15-24. · Zbl 0895.90126
[4] Club CRIN Logique Floue. Evaluation subjective?Méthodes, applications et enjeux. Association ECRIN, 32 bd de Vaugirard, 75015 Paris.
[5] A possibilistic logic machinery for qualitative decision. In: Proceedings of the AAAI’97 Spring Symposium Series on Qualitative Preferences in Deliberation and Practical Reasoning, Stanford, CA, 1997, p 47-54.
[6] Dubois, Fuzzy Sets Syst 28 pp 313– (1988) · Zbl 0658.94026
[7] Assessing the value of a candidate. A qualitative possibilistic approach. In: Symbolic and Quantitative Approaches to Reasoning and Uncertainty Proc. (ECSQARU 99), LNAI 1638, Berlin: Springer Verlag, 1999, p 137-147.
[8] Assessing the value of a candidate. Comparing belief functions and possibility theories. In: Proceedings of the XVth Conference, Uncertainty in Artificial Intelligence, San Francisco: Morgan Kaufmann; 1999, p 170-177.
[9] Dubois, Inform Sci pp 30– (1983)
[10] Dubois, Contr Eng Prac 2 pp 811– (1994)
[11] Decision making under fuzzy constraints and fuzzy criteria-mathematical programming versus rulebased approach. In: editors. Fuzzy optimization-recent advances. Heidelberg: Physica-Verlag; 1994, p 21-32.
[12] Possibility theory as a basis for qualitative decision theory. In: Proc. of the 14th Int. Joint Conf. on Artificial Intelligence (IJCAI’95), Montréal, Canada, 1995, p 1924-1930.
[13] Fuzzy criteria and fuzzy rules in subjective evaluations?A general discussion. In: Proceedings of the 5th European Congress on Intelligent Technologies and Soft Computing (EUFIT 97), Aachen, Germany, 1997, p 975-978.
[14] Possibility theory: Qualitative and quantitative aspects. In: editor. Quantified representation of uncertainty and imprecision. Handbook of defeasible reasoning and uncertainty management systems. series editors. Dordrecht: Kluwer Academic Publishers; Vol. 1, 1998, p 169-226.
[15] Qualitative decision theory with Sugeno integrals. In: Proc. of the 14th Conf. Uncertainty in Artificial Intelligence (UAI’98), Madison, USA, 24-26 July 1998, Morgan Kaufmann, Los Altos, CA, p 121-128.
[16] Positive and negative explanations of uncertain reasoning in the framework of possibility theory. In: editors. Fuzzy logic for the management of uncertainty. New York: Wiley; 1992, p 319-333.
[17] Fundamentals of uncertainty calculi with applications to fuzzy inference. Dordrecht: Kluwer Academic Publishers; 1995.
[18] Application of the Choquet integral in multiattribute decision making. In: Fuzzy measures and integrals. Theory and applications. Heidelberg: Physica-Verlag; 2000, p 348-374.
[19] Empirical evaluation of possibility theory in human radiological diagnosis. In: Proc. of the 13th Eur. Conf. on Artificial Intelligence (ECAI’98), Brighton, UK, New York: Wiley, 1998, p 124-128.
[20] A mathematical theory of evidence. Princeton, NJ: Princeton University Press; 1976. · Zbl 0359.62002
[21] Smets, Int J Approx Reason 9 pp 1– (1993) · Zbl 0796.68177
[22] No Dutch Book can be built against the TBM even though update is not obtained by Bayes rule of conditioning. In: Scozzafava R, editor. SIS, Workshop on Probabilistic Expert Systems, Roma, 1993, p 181-204.
[23] Smets, Artificial Intell 66 pp 191– (1994) · Zbl 0807.68087
[24] The transferable belief model for quantified belief representation. In: editors. Handbook of defeasible reasoning and uncertainty management systems. Vol. 1, Dordrecht: Kluwer Academic Publishers; 1998, p 267-301.
[25] Fuzzy measures and fuzzy integrals: A survey. In: Fuzzy automata and decision process. Amsterdam: North-Holland; 1977, p 89-102.
[26] Xu, IEEE Trans Syst Man and Cybernet 26A pp 599– (1996)
[27] Zadeh, Inform. Sci. 8 pp 199– (1975) · Zbl 0397.68071
[28] Part 2, 1975; 8:301-375;
[29] Part 3, 1975; 1975:43-80.
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