Making decisions from weighted arguments

  • Leila Amgoud
  • Henri Prade
Part of the CISM International Centre for Mechanical Sciences book series (CISM, volume 482)


Humans currently use arguments for explaining choices which are already made, or for evaluating potential choices. Each potential choice has usually pros and cons of various strengths. In spite of the usefulness of arguments in a decision making process, there have been few formal proposals handling this idea if we except works by Fox and Parsons and by Bonet and Geffner. In this paper we propose a possibilistic logic framework where arguments are built from a knowledge base with uncertain elements and a set of prioritized goals. The proposed approach can compute two kinds of decisions by distinguishing between pessimistic and optimistic attitudes. When the available, maybe uncertain, knowledge is consistent, as well as the set of prioritized goals (which have to be fulfilled as far as possible), the method for evaluating decisions on the basis of arguments agrees with the possibility theory-based approach to decision-making under uncertainty. Taking advantage of its relation with formal approaches to defeasible argumentation, the proposed framework can be generalized in case of partially inconsistent knowledge, or goal bases.

Key words

Decision Argumentation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    L. Amgoud and C. Cayrol. Inferring from inconsistency in preference-based argumentation frameworks. International Journal of Automated Reasoning, Volume 29,N2:125–169, 2002.zbMATHCrossRefMathSciNetGoogle Scholar
  2. [2]
    L. Amgoud and C. Cayrol. A reasoning model based on the production of acceptable arguments. Annals of Mathematics and Artificial Intelligence, 34:197–216, 2002.zbMATHCrossRefMathSciNetGoogle Scholar
  3. [3]
    Salem Benferhat, Didier Dubois, and Henri Prade. Representing default rules in possibilistic logic. In Proceedings of the 3rd International Conference on Knowledge Representation and Reasoning, pages 673–684, 1992.Google Scholar
  4. [4]
    B. Bonet and H. Geffner. Arguing for decisions: A qualitative model of decision making. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence, pages 98–105, 1996.Google Scholar
  5. [5]
    C. Boutilier. Towards a logic for qualitative decision theory. In Proceedings of the 4th International Conference on Knowledge Representation and Reasoning, pages 75–86, 1994.Google Scholar
  6. [6]
    C. Chesnevar, A. Maguitman, and R. P. Loui. Logical models of arguments. A CM computing surveys, 32:4:337–383, 2000.CrossRefGoogle Scholar
  7. [7]
    D. Dubois, M. Grabisch, F. Modave, and H. Prade. Relating decision under uncertainty and multicriteria decision making models. International Journal of Intelligent Systems, 15:967–979, 2000.zbMATHCrossRefGoogle Scholar
  8. [8]
    D. Dubois, J. Lang, and H. Prade. Automated reasoning using possibilistic logic: semantics, belief revision and variable certainty weights. IEEE Trans. on Data and Knowledge Engineering, 6:64–71, 1994.CrossRefGoogle Scholar
  9. [9]
    D. Dubois and H. Prade. Possibility theory as a basis for qualitative decision theory. In 14th Inter. Joint Conf. on Artificial Intelligence (IJCAI’95), pages 1924–1930, Montréal, August 20–25 1995. Morgan Kaufmann, San Mateo, CA.Google Scholar
  10. [10]
    D. Dubois, H. Prade, and R. Sabbadin. A possibilistic logic machinery for qualitative decision. In Working Notes AAAI’97 Workshop on Qualitative Preferences in Deliberation and Practical Reasoning, pages 47–54, Stanford, Mar. 24–26 1997. AAAI Press, Menlo Park.Google Scholar
  11. [11]
    Didier Dubois, Daniel Le Berre, Henri Prade, and Rgis Sabbadin. Using possibilistic logic for modeling qualitative decision: Atms-based algorithms. Fundamenta Informaticae, 37:1–30, 1999.zbMATHMathSciNetGoogle Scholar
  12. [12]
    Didier Dubois, Luis Godo, Henri Prade, and A. Zapico. On the possibilistic decision model: from decision under uncertainty to case-based decision. Int. J. of Uncert., Fuzziness and Knowledge-Based Syst., 7:6:631–670, 1999.zbMATHCrossRefMathSciNetGoogle Scholar
  13. [13]
    Didier Dubois, Henri Prade, and Rgis Sabbadin. Decision-theoretic foundations of qualitative possibility theory. European Journal of Operational Research, 128:459–478, 2001.zbMATHCrossRefMathSciNetGoogle Scholar
  14. [14]
    P. M. Dung. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77:321–357, 1995.zbMATHCrossRefMathSciNetGoogle Scholar
  15. [15]
    H. Fargier and R. Sabbadin. Qualitative decision under uncertainty: back to expected utility. In Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI’03, 2003.Google Scholar
  16. [16]
    J. Fox and S. Das. Safe and Sound. Artificial Intelligence in Hazardous Applications. AAAI Press, The MIT Press, 2000.Google Scholar
  17. [17]
    J. Fox and S. Parsons. On using arguments for reasoning about actions and values. In Magdeburg Proceedings of the AAAI Spring Symposium on Qualitative Preferences in Deliberation and Practical Reasoning, Stanford, 1997.Google Scholar
  18. [18]
    B. Franklin. Letter to j. b. priestley, 1772, in the complete works, j. bigelow, ed.,. New York: Putnam, page 522, 1887.Google Scholar
  19. [19]
    S. Kraus, D. Lehmann, and M. Magidor. Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence, 44:167–207, 1990.CrossRefMathSciNetGoogle Scholar
  20. [20]
    G. R. Simari and R. P. Loui. A mathematical treatment of defeasible reasoning and its implementation. Artificial Intelligence and Law, 53:125–157, 1992.CrossRefMathSciNetGoogle Scholar
  21. [21]
    S. W. Tan and J. Pearl. Qualitative decision theory. In Proceedings of the 11th National Conference on Artificial Intelligence, pages 928–933, 1994.Google Scholar

Copyright information

© CISM, Udine 2006

Authors and Affiliations

  • Leila Amgoud
    • 1
  • Henri Prade
    • 1
  1. 1.IRIT - UPSToulouseFrance

Personalised recommendations