Bipolar Representations in Reasoning, Knowledge Extraction and Decision Processes

  • Didier Dubois
  • Henri Prade
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)


This paper surveys various areas in information engineering where an explicit handling of positive and negative sides of information is appropriate. Three forms of bipolarity are laid bare. They can be instrumental in logical representations of incompleteness, rule representation and extraction, argumentation, and decision analysis.


Classical Logic Positive Information Negative Information Belief Base Possibility Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Amgoud, L., Prade, H.: Comparing decisions on the basis of a bipolar typology of arguments. In: Hunter, A., Dix, J. (eds.) Proc. 11th Workshop on Nonmonotonic Reasoning, Windermere U.K., pp. 426–432 (2006)Google Scholar
  2. 2.
    Banerjee, M.: Rough sets and 3-valued Lukasiewicz logic. Fundamenta Informaticae 32, 213–220 (1997)Google Scholar
  3. 3.
    Belnap, N.D.: A useful four-valued logic. In: Dunn, J.M., Epstein, G. (eds.) Modern Uses of Multiple-Valued Logic, pp. 8–37. D. Reidel, Dordrecht (1977)Google Scholar
  4. 4.
    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
  5. 5.
    Blamey, S.: Partial logic. In: Gabbay, D., Guentner, F. (eds.) Handbook of Philosophical Logic, 2nd edn., vol. 5, pp. 261–353. Kluwer Academic Publ., Dordrecht (1998)Google Scholar
  6. 6.
    Cacioppo, J.T., Gardner, W.L., Berntson, G.G.: Beyond bipolar conceptualizations and measures: The case of attitudes and evaluative space. Personality and Social Psychology Review 1, 3–25 (1997)CrossRefGoogle Scholar
  7. 7.
    Dubois, D., Fargier, H.: Qualitative decision making with bipolar information. In: Proc. of Int. Conf. on Principles of Knowledge Representation and Reasoning KR 2006, Windermere, UK, pp. 175–186. AAAI Press, Menlo Park (2006)Google Scholar
  8. 8.
    Dubois, D., Hajek, P., Prade, H.: Knowledge-driven versus data-driven logics. J. of Logic, Language, and Information 9, 65–89 (2000)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Dubois, D., Huellermeier, E., Prade, H.: A systematic approach to the assessment of fuzzy association rules. Data Mining and Knowledge Discovery (to appear, 2006)Google Scholar
  10. 10.
    Dubois, D., Prade, H.: Conditional objects as non-monotonic consequence relationships. IEEE Trans. on Systems, Man and Cybernetics 24, 1724–1739 (1994)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Dubois, D., Prade, H.: Possibility theory, probability theory and multiple-valued logics: A clarification. Ann. Math. and Artificial Intelligence 32, 35–66 (2001)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Dubois, D., Prade, H., Smets, P.: "Not impossible" vs. "Guaranteed possible" in fusion and revision. In: Benferhat, S., Besnard, P. (eds.) ECSQARU 2001. LNCS (LNAI), vol. 2143, pp. 522–531. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  13. 13.
    Dubois, D., Prade, H., Ughetto, L.: A new perspective on reasoning with fuzzy rules. Int. J. of Intelligent Systems 18, 541–567 (2003)MATHCrossRefGoogle Scholar
  14. 14.
    Grabisch, M.: The Moebius transform on symmetric ordered structures and its application to capacities on finite sets. Discrete Math. 28(1-3), 17–34 (2004)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Grabisch, M., Labreuche, C.: Bi-capacities — parts I and II. Fuzzy Sets and Systems 151(2), 211–260 (2005)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Greco, S., Matarazzo, B., Slowinski, R.: Bipolar Sugeno and Choquet integrals. In: EUROFUSE Workshop on Information Systems, Varenna, pp. 191–196 (2002)Google Scholar
  17. 17.
    Kraus, S., Lehmann, D., Magidor, M.: Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence 44(1-2), 167–207 (1990)MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Labreuche, C., Grabisch, M.: Generalized Choquet-like aggregation functions for handling bipolar scales. Eur. J. of Operational Research 172(3), 931–955 (2006)MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Osgood, C.E., Suci, G.J., Tannenbaum, P.H.: The Measurement of Meaning. Univ. of Illinois Press, Chicago (1957)Google Scholar
  20. 20.
    Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publ., Dordrecht (1991)MATHGoogle Scholar
  21. 21.
    Prade, H., Serrurier, M.: Version space learning for possibilistic hypotheses. In: Proc. of 17th Europ. Conf. on Art. Intel., ECAI 2006, Riva del Garda, Italy (2006)Google Scholar
  22. 22.
    Slovic, P., Finucane, M., Peters, E., MacGregor, D.G.: Rational actors or rational fools? implications of the affect heuristic for behavioral economics. The Journal of Socio-Economics 31, 329–342 (2002)CrossRefGoogle Scholar
  23. 23.
    Tversky, A., Kahneman, D.: Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty 5, 297–323 (1992)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Didier Dubois
    • 1
  • Henri Prade
    • 1
  1. 1.IRITToulouseFrance

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