Argumentation for Decision Making

  • Leila Amgoud

Decision making, often viewed as a form of reasoning toward action, has raised the interest of many scholars including economists, psychologists, and computer scientists for a long time. Any decision problem amounts to selecting the “best” or sufficiently “good” action(s) that are feasible among different alternatives, given some available information about the current state of the world and the consequences of potential actions. Available information may be incomplete or pervaded with uncertainty. Besides, the goodness of an action is judged by estimating how much its possible consequences fit the preferences of the decision maker. This agent is assumed to behave in a rational way [29] amgoud-woold, at least in the sense that his decisions should be as much as possible consistent with his preferences.


Decision System Colonic Polyp Argumentation Framework Practical Argument Epistemic System 
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.
    L. Amgoud. A general argumentation framework for inference and decision making. In 21st Conference on Uncertainty in Artificial Intelligence, UAI’2005, pages 26–33, 2005.Google Scholar
  2. 2.
    L. Amgoud, J-F. Bonnefon, and H. Prade. An argumentation-based approach to multiple criteria decision. In Proceedings of the 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU’05), pages 269–280, 2005.Google Scholar
  3. 3.
    L. Amgoud and C. Cayrol. Inferring from inconsistency in preference-based argumentation frameworks. In International Journal of Automated Reasoning, 29, N2:125–169, 2002.MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    L. Amgoud and C. Cayrol. A reasoning model based on the production of acceptable arguments. In Annals of Mathematics and Artificial Intelligence, 34:197–216, 2002.MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    L. Amgoud and H. Prade. Explaining qualitative decision under uncertainty by argumentation. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI’06), pages 219–224, 2006.Google Scholar
  6. 6.
    K. Atkinson. Value-based argumentation for democratic decision support. In Proceedings of the First International Conference on Computational Models of Natural Argument (COMMA’06), pages 47–58, 2006.Google Scholar
  7. 7.
    K. Atkinson, T. Bench-Capon, and P. McBurney. Justifying practical reasoning. In Proceedings of the Fourth Workshop on Computational Models of Natural Argument (CMNA’04), pages 87–90, 2004.Google Scholar
  8. 8.
    P. Baroni, M. Giacomin, and G. Guida. Scc-recursiveness: a general schema for argumentation semantics. In Artificial Intelligence, 168 (1-2):162–210, 2005.MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Ph. Besnard and A. Hunter. A logic-based theory of deductive arguments. In Artificial Intelligence, 128:203–235, 2001.MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    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 (UAI’96), pages 98–105, 1996.Google Scholar
  11. 11.
    J.-F. Bonnefon and H. Fargier. Comparing sets of positive and negative arguments: Empirical assessment of seven qualitative rules. In Proceedings of the 17th European Conference on Artificial Intelligence (ECAI’06), pages 16–20, 2006.Google Scholar
  12. 12.
    M. Bratman. Intentions, plans, and practical reason. Harvard University Press, Massachusetts, 1987.Google Scholar
  13. 13.
    J.T. Cacioppo, W.L. Gardner, and G.G. Bernston. Beyond bipolar conceptualizations and measures: The case of attitudes and evaluative space. In Personality and Social Psychology Review, 1:3–25, 1997.CrossRefGoogle Scholar
  14. 14.
    C. Cayrol, V. Royer, and C. Saurel. Management of preferences in assumption-based reasoning. In Lecture Notes in Computer Science, 682:13–22, 1993.Google Scholar
  15. 15.
    Y. Dimopoulos, P. Moraitis, and A. Tsoukias. Argumentation based modeling of decision aiding for autonomous agents. In IEEE-WIC-ACM International Conference on Intelligent Agent Technology, pages 99–105, 2004.Google Scholar
  16. 16.
    D. Dubois and H. Fargier. Qualitative decision making with bipolar information. In Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning (KR’06), pages 175–186, 2006.Google Scholar
  17. 17.
    P. M. Dung. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. In Artificial Intelligence, 77:321–357, 1995.MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    M. Elvang-Goransson, J. Fox, and P. Krause. Dialectic reasoning with inconsistent information. In Proceedings of 9th Conference on Uncertainty in Artificial Intelligence (UAI’93), pages 114 – 121, 1993.Google Scholar
  19. 19.
    J. Fox and S. Das. Safe and Sound. Artificial Intelligence in Hazardous Applications. AAAI Press, The MIT Press, 2000.Google Scholar
  20. 20.
    J. Fox and S. Parsons. On using arguments for reasoning about actions and values. In Proceedings of the AAAI Spring Symposium on Qualitative Preferences in Deliberation and Practical Reasoning, Stanford, 1997.Google Scholar
  21. 21.
    R. Girle, D. Hitchcock, P. McBurney, and B. Verheij. Decision support for practical reasoning. C. Reed and T. Norman (Editors): Argumentation Machines: New Frontiers in Argument and Computation. Argumentation Library. Dordrecht, The Netherlands: Kluwer Academic, 2003.Google Scholar
  22. 22.
    T. Gordon and G. Brewka. How to buy a porsche: An approach to defeasible decision making (preliminary report). In In the workshop of Comutational Dialectics, 1994.Google Scholar
  23. 23.
    T. F. Gordon and N. I. Karacapilidis. The Zeno Argumentation Framework. Kunstliche Intelligenz, 13(3):20–29, 1999.Google Scholar
  24. 24.
    J. Pollock. The logical foundations of goal-regression planning in autonomous agents. In Artificial Intelligence, 106(2):267–334, 1998.MATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    J. Raz. Practical reasoning. Oxford, Oxford University Press, 1978.Google Scholar
  26. 26.
    G. R. Simari and R. P. Loui. A mathematical treatment of defeasible reasoning and its implementation. In Artificial Intelligence and Law, 53:125–157, 1992.CrossRefMathSciNetGoogle Scholar
  27. 27.
    S. W. Tan and J. Pearl. Qualitative decision theory. In Proceedings of the 11th National Conference on Artificial Intelligence (AAAI’94), pages 928–933, 1994.Google Scholar
  28. 28.
    D. Walton. Argument schemes for presumptive reasoning, volume 29. Lawrence Erlbaum Associates, Mahwah, NJ, USA, 1996.Google Scholar
  29. 29.
    M. J. Wooldridge. Reasoning about rational agents. MIT Press, Cambridge Massachusetts, London England, 2000.MATHGoogle Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Institut de Recherche en Informatique de Toulouse IRIT-UPS31062 ToulouseFrance

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