A Possibilistic Logic View of Sugeno Integrals
Sugeno integrals are well-known qualitative aggregation functions in multiple criteria decision making. They return a global evaluation between the minimum and the maximum of the input criteria values. They can model sophisticated aggregation schemes through a system of priorities that applies to any subset of criteria and can take into account some kind of synergy inside subsets of criteria. Although a given Sugeno integral specifies a particular way of implicitly describing a set of entities reaching some global satisfaction level, it is hard to figure out what is the underlying explicit meaning of such an integral in practice (even if the priority level associated to each subset of criteria has a precise meaning). The paper proposes an answer to this problem. Any capacity on a finite set can be represented by a special possibilistic logic base containing positive prioritised clauses, and conversely any possibilistic logic base can represent a set-function. Moreover, Sugeno integral can be represented by a possibilistic logic base expressing how it behaves (thanks to a mapping between the scale and a set of logical atoms reflecting the different values for each criterion). Viewing a Sugeno integral as a set of prioritized logically expressed goals has not only the advantage to make the contents of a Sugeno integral more readable, but it also prompts Sugeno integrals into the realm of logic, and makes it possible to define entailment between them.
KeywordsPriority Level Propositional Variable Fuzzy Measure Possibility Distribution Possibilistic Logic
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- 1.Benferhat, S., Dubois, D., Kaci, S., Prade, H.: Bipolar possibility theory in preference modeling: Representation. Fusion and Optimal Solutions, Information Fusion 7, 135–150 (2006)Google Scholar
- 7.Gérard, R., Kaci, S., Prade, H.: Ranking alternatives on the basis of generic constraints and examples - A possibilistic approach. In: Veloso, M.M. (ed.) Proc. 20th Inter. Joint Conf. on Artificial Intelligence (IJCAI 2007), Hyderabad, January 6-12, pp. 393–398 (2007)Google Scholar
- 12.Marichal, J.-L.: Aggregation Operations for Multicriteria Decision Aid. Ph.D.Thesis, University of Liège, Belgium (1998)Google Scholar
- 13.Mesiar, R.: k-order Pan-discrete fuzzy measures. In: Proc. 7th Inter. Fuzzy Systems Assoc. World Congress (IFSA 1997), Prague, June 25-29, vol. 1, pp. 488–490 (1997)Google Scholar
- 14.Sugeno, M.: Theory of Fuzzy Integrals and its Applications, Ph.D. Thesis, Tokyo Institute of Technology, Tokyo (1974)Google Scholar
- 15.Sugeno, M.: Fuzzy measures and fuzzy integrals: a survey. In: Gupta, M.M., Saridis, G.N., Gaines, B.R. (eds.) Fuzzy Automata and Decision Processes, pp. 89–102. North-Holland, Amsterdam (1977)Google Scholar
- 16.Prade, H., Rico, A.: Describing acceptable objects by means of Sugeno integrals. In: Martin, T., et al. (eds.) Proc. 2nd IEEE Inter. Conf. of Soft Computing and Pattern Recognition (SoCPaR 2010), Cergy Pontoise, Paris, December 7-10 (2010)Google Scholar