Autonomous Agents and Multi-Agent Systems

, Volume 5, Issue 3, pp 329–363 | Cite as

Utilitarian Desires

  • Jérôme Lang
  • Leendert van der Torre
  • Emil Weydert


Autonomous agents reason frequently about preferences such as desires and goals. In this paper we propose a logic of desires with a utilitarian semantics, in which we study nonmonotonic reasoning about desires and preferences based on the idea that desires can be understood in terms of utility losses (penalties for violations) and utility gains (rewards for fulfillments). Our logic allows for a systematic study and classification of desires, for example by distinguishing subtly different ways to add up these utility losses and gains. We propose an explicit construction of the agent's preference relation from a set of desires together with different kinds of knowledge. A set of desires extended with knowledge induces a set of ‘distinguished’ utility functions by adding up the utility losses and gains of the individual desires, and these distinguished utility functions induce the preference relation.

qualitative decision theory QDT nonmonotonic reasoning about preferences autonomous agents BDI logic logic of desires utilitarian desires 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    F. Bacchus and A. Grove, “Utility independence in a qualitative decision theory,” in Proc. Fifth Int.Conf. Knowledge Representation and Reasoning (KR'96), 1996, pp. 542-552.Google Scholar
  2. 2.
    J. Bell and Z. Huang, “Dynamic goal hierarchies,” in Intelligent Agent Systems, Theoretical and Practical Issues, 1997, pp. 88-103.Google Scholar
  3. 3.
    M. Belzer, “A logic of deliberation,” in Proc. Fifth National Conf. Artificial Intelligence (AAAI'86), 1986, pp. 38-43.Google Scholar
  4. 4.
    S. Benferhat, C. Cayrol, D. Dubois, J. Lang, and H. Prade, “Inconsistency management and prioritized syntax-based entailment,” in Proc. Thirteenth Int. Joint Conf. Artificial Intelligence (IJCAI'93), 1993, pp. 640-645.Google Scholar
  5. 5.
    C. Boutilier, “Towards a logic for qualitative decision theory,” in Proc. Fourth Int. Conf. Knowledge Representation and Reasoning (KR'94), 1994, pp. 75-86.Google Scholar
  6. 6.
    C. Castelfranchi, F. Dignum, C. Jonker, and J. Treur, “Deliberate normative agents: principles and architecture,” in Intelligent Agents VI. Proc. Sixth Int. Workshop on Agent Theories, Architectures and Languages, ATAL'99, 2000.Google Scholar
  7. 7.
    L. Cholvy and F. Cuppens, “Solving normative con.icts by merging roles,” in Proc. Fifth Int. Conf. Artificial Intelligence and Law (ICAIL'95), Washington, 1995.Google Scholar
  8. 8.
    P. Cohen and H. Levesque, “Intention is choice with commitment,” Artificial Intelligence, vol. 42, no. 2-3, pp. 213-261, 1990.Google Scholar
  9. 9.
    T. Dean and M. Wellman, Planning and Control, Morgan Kaufmann: San Mateo, 1991.Google Scholar
  10. 10.
    J. Doyle, “A model for deliberation, action and introspection,” Technical Report AI-TR-581, MIT AI Laboratory, 1980.Google Scholar
  11. 11.
    J. Doyle, “Rationality and its rules in reasoning (extended abstract),” in Proc. Tenth National Conf. Artificial Intelligence (AAAI'91), 1991, pp. 1093-1100.Google Scholar
  12. 12.
    J. Doyle and R. Thomason, “Background to qualitative decision theory,” AI Magazine, vol. 20, no. 2, pp. 55-68, 1999.Google Scholar
  13. 13.
    J. Doyle and M. Wellman, “Preferential semantics for goals,” in Proc. Tenth National Conf. Artificial Intelligence (AAAI'91), 1991, pp. 698-703.Google Scholar
  14. 14.
    J. Doyle, Y. Shoham, and M. Wellman, “The logic of relative desires,” in Sixth Int. Symposium on Methodologies for Intelligent Systems, Charlotte, NC, 1991.Google Scholar
  15. 15.
    D. Dubois and H. Prade, “Possibility theory as a basis for qualitative decision theory,” in Proc. Fourteenth Int. Joint Conf. Artificial Intelligence (IJCAI'95), 1995, pp. 1924-1930.Google Scholar
  16. 16.
    M. Gelfond and V. Lisfchitz, “Representing action and change by logic programs,” in J. Logic Programming, vol. 17, pp. 301-322, 1993.Google Scholar
  17. 17.
    M. Goldszmidt, P. Morris, and J. Pearl, “A maximum entropy approach to nonmonotonic reasoning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 3, pp. 220-232, 1993.Google Scholar
  18. 18.
    P. Haddawy and S. Hanks, “Representations for decision-theoretic planning: utility functions for dead-line goals,” in Proc. Third Int. Conf. Knowledge Representation and Reasoning (KR'92), Cambridge, MA, 1992.Google Scholar
  19. 19.
    B. Hansson, “An analysis of some deontic logics,” in R. Hilpinen (ed.), Deontic Logic: Introductory and Systematic Readings, D. Reidel Publishing Company: Dordrecht, Holland, 1971, pp. 121-147.Google Scholar
  20. 20.
    J. Horty, “Moral dilemmas and nonmonotonic logic,” J. Philosophical Logic, vol. 23, pp. 35-65, 1994.Google Scholar
  21. 21.
    N. Jennings and J. Campos, “Towards a social level characterisation of socially responsible agents,” IEEE Proc. Software Engeneering, vol. 144, no. 1, pp. 11-25, 1997.Google Scholar
  22. 22.
    R. Keeney and H. Raiffa, Decisions with Multiple Objectives: Preferences and Value Trade-offs, John Wiley and Sons: New York, 1976.Google Scholar
  23. 23.
    J. Lang, “Conditional desires and utilities-an alternative approach to qualitative decision theory,” in Proc. Twelth European Conf. Artificial Intelligence (ECAI'96), 1996, pp. 318-322.Google Scholar
  24. 24.
    D. Lehmann, “Generalized qualitative probability: Savage revisited,” in Proc. Twelth Conf. Uncertainty in Artificial Intelligence (UAI'96), 1996, pp. 381-388.Google Scholar
  25. 25.
    D. Lehmann, “Non-standard numbers for qualitative decision making,” in Proc. Seventh Conf. Theoretical Aspects of Rationality and Knowledge (TARK'98), 1998, pp. 161-174.Google Scholar
  26. 26.
    D. Lehmann and M. Magidor, “What does a conditional knowledge base entail?” Artificial Intelligence, vol. 55, pp. 1-60, 1992.Google Scholar
  27. 27.
    D. Makinson, “Five faces of minimality,” Studia Logica, vol. 52, pp. 339-379, 1993.Google Scholar
  28. 28.
    D. Makinson, “General patterns in nonmonotonic reasoning,” in Gabbay, Hogger, and Robinson (eds.), Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 3. Oxford University Press: Oxford, 1994, pp. 35-110.Google Scholar
  29. 29.
    R. Neapolitan, Probabilistic Reasoning in Expert Systems, John Wiley and Sons: New York, 1990.Google Scholar
  30. 30.
    J. Pearl, “From conditional ought to qualitative decision theory,” in Proc. Ninth Conf. Uncertainty in Artificial Intelligence (UAI'93), 1993, pp. 12-20.Google Scholar
  31. 31.
    H. Prakken and M. Sergot, “Contrary-to-duty obligations,” Studia Logica, vol. 57, pp. 91-115, 1996.Google Scholar
  32. 32.
    A. Rao and M. Georgeff, “Modeling rational agents within a BDI architecture,” in Proc. Second Int. Conf. Knowledge Representation and Reasoning (KR'91), 1991, pp. 473-484.Google Scholar
  33. 33.
    S.-W. Tan and J. Pearl, “Qualitative decision theory,” in Proc. Thirteenth National Conf. Artificial Intelligence (AAAI'93), 1994a.Google Scholar
  34. 34.
    S.-W. Tan and J. Pearl, “Specification and evaluation of preferences under uncertainty,” in Proc. Fourth Int. Conf. Knowledge Representation and Reasoning (KR'94), 1994b, pp. 530-539.Google Scholar
  35. 35.
    Y. Tan and L. van der Torre, “How to combine ordering and minimizing in a deontic logic based on preference,” in Deontic Logic, Agency and Normative Systems. Proc. Third Workshop on Deontic Logic in Computer Science (4eon'96), 1996, pp. 216-232.Google Scholar
  36. 36.
    R. Thomason, “Desires and defaults: a framework for planning with inferred goals,” in Proc. Seventh Int. Conf. Knowledge Representation and Reasoning (KR'2000), 2000, pp. 702-713.Google Scholar
  37. 37.
    R. Thomason and R. Horty, “Nondeterministic action and Dominance: foundations for planning and qualitative decision,” in Proc. Theoretical Aspects of Reasoning about Knowledge (TARK'96), 1996, pp. 229-250.Google Scholar
  38. 38.
    L. van der Torre, “Violated obligations in a defeasible deontic logic,” in Proc. Eleventh European Conf. Artificial Intelligence (ECAI'94), 1994, pp. 371-375.Google Scholar
  39. 39.
    L. van der Torre, “Labeled logics of goals,” in Proc. Thirteenth European Conf. Artificial Intelligence (ECAI'98), 1998, pp. 368-369.Google Scholar
  40. 40.
    L. van der Torre and Y. Tan, “Contrary-to-duty reasoning with preference-based dyadic obligations,” Annals of Mathematics and Artificial Intelligence, vol. 27, pp. 49-78, 1999a.Google Scholar
  41. 41.
    L. van der Torre and Y. Tan, “Diagnosis and decision making in normative reasoning,” Artificial Intelligence and Law, vol. 7, pp. 51-67, 1999b.Google Scholar
  42. 42.
    L. van der Torre and E. Weydert, “Parameters for utilitarian desires in a qualitative decision theory,” Applied Intelligence, 2000.Google Scholar
  43. 43.
    J. von Neumann and O. Morgenstern, Theories of Games and Economic Behavior, Princeton University Press, 1944.Google Scholar
  44. 44.
    G. von Wright, Norms Truth and Logic. Practical Reason, Blackwell: Oxford, 1983.Google Scholar
  45. 45.
    J. Wainer, “Yet another semantics for goals and goal priorities,” in Proc. Eleventh European Conf. Artificial Intelligence (ECAI'94), 1994, pp. 269-273.Google Scholar
  46. 46.
    E. Weydert, “System JZ: How to build a canonical ranking model of a default knowledge base,” in Proc. Seventh Int. Conf. Knowledge Representation and Reasoning (KR'98), 1998, pp. 190-201.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Jérôme Lang
    • 1
  • Leendert van der Torre
    • 2
  • Emil Weydert
    • 3
  1. 1.Institut de Recherche en Informatique de ToulouseUniversité Paul SabatierToulouse Cedex (France
  2. 2.Vrije UniversiteitAmsterdamThe Netherlands
  3. 3.Max-Planck-Institute for Computer ScienceSaarbrückenGermany

Personalised recommendations