International Conference on Algorithmic DecisionTheory

ADT 2015: Algorithmic Decision Theory pp 52-68 | Cite as

Towards Decision Making via Expressive Probabilistic Ontologies

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9346)

Abstract

We propose a framework for automated multi-attribute decision making, employing the probabilistic non-monotonic description logics proposed by Lukasiewicz in 2008. Using this framework, we can model artificial agents in decision-making situation, wherein background knowledge, available alternatives and weighted attributes are represented via probabilistic ontologies. It turns out that extending traditional utility theory with such description logics, enables us to model decision-making problems where probabilistic ignorance and default reasoning plays an important role. We provide several decision functions using the notions of expected utility and probability intervals, and study their properties.

References

  1. 1.
    Bienvenu, M., Lang, J., Wilson, N.: From preference logics to preference languages, and back. In: Proceedings of the International Conference on Principles and Knowledge Representation and Reasoning KR (2010)Google Scholar
  2. 2.
    Boutilier, C.: Toward a logic for qualitative decision theory. In: Proceedings of the International Conference on Principles and Knowledge Representation and Reasoning KR (1994)Google Scholar
  3. 3.
    Chevaleyre, Y., Endriss, U., Lang, J.: Expressive power of weighted propositional formulas for cardinal preference modeling. In: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning, KR (2006)Google Scholar
  4. 4.
    Delgrande, J.P., Schaub, T.: Expressing preferences in default logic. Artif. Intell. 123(1–2), 41–87 (2000)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Delgrande, J.P., Schaub, T., Tompits, H., Wang, K.: A classification and survey of preference handling approaches in nonmonotonic reasoning. Comput. Intell. 20(2), 308–334 (2004)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Ellsberg, D.: Risk, ambiguity, and the savage axioms. Q. J. Econ. 75, 643–669 (1961)CrossRefGoogle Scholar
  7. 7.
    Fishburn, P.C.: Utility Theory for Decision Making. Robert E. Krieger Publishing Co., Huntington, New York (1969)Google Scholar
  8. 8.
    Huntley, N., Hable, R., Troffaes, M.C.M.: Decision making. In: Augustin, T., Coolen, F.P.A., de Cooman, G., Troffaes, M.C.M. (eds.) Introduction to Imprecise Probabilities, pp. 190–206. Wiley, Chichester (2014)Google Scholar
  9. 9.
    Kaci, S., van der Torre, L.: Reasoning with various kinds of preferences: logic, non-monotonicity, and algorithms. Ann. OR 163(1), 89–114 (2008)CrossRefMATHGoogle Scholar
  10. 10.
    Keeney, R.L., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York (1976)Google Scholar
  11. 11.
    Lafage, C., Lang, J.: Logical representation of preferences for group decision making. In: Proceedings of the International Conference on Principles and Knowledge Representation and Reasoning KR, San Francisco (2000)Google Scholar
  12. 12.
    Levi, I.: The Enterprise of Knowledge. MIT Press, Cambridge, MA (1980)Google Scholar
  13. 13.
    Lukasiewicz, T.: Expressive probabilistic description logics. Artif. Intell. 172(6–7), 852–883 (2008)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Lukasiewicz, T., Martinez, M.V., Simari, G.I.: Probabilistic preference logic networks. In: Proceedings of the European Conference on Artificial Intelligence ECAI (2014)Google Scholar
  15. 15.
    Di Noia, T., Lukasiewicz, T.: Combining CP-nets with the power of ontologies. In: AAAI (Late-Breaking Developments) (2013)Google Scholar
  16. 16.
    Ragone, A., Di Noia, T., Donini, F.M., Di Sciascio, E., Wellman, M.P.: Computing utility from weighted description logic preference formulas. In: Baldoni, M., Bentahar, J., van Riemsdijk, M.B., Lloyd, J. (eds.) DALT 2009. LNCS, vol. 5948, pp. 158–173. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  17. 17.
    Ragone, A., Di Noia, T., Donini, F.M., Di Sciascio, E., Wellman, M.P.: Weighted description logics preference formulas for multiattribute negotiation. In: Godo, L., Pugliese, A. (eds.) SUM 2009. LNCS, vol. 5785, pp. 193–205. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  18. 18.
    Straccia, U.: Multi criteria decision making in fuzzy description logics: a first step. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds.) KES 2009, Part I. LNCS, vol. 5711, pp. 78–86. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  19. 19.
    Uckelman, J., Chevaleyre, Y., Endriss, U., Lang, J.: Representing utility functions via weighted goals. Math. Log. Q. 55(4), 341–361 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Erman Acar
    • 1
  • Camilo Thorne
    • 1
  • Heiner Stuckenschmidt
    • 1
  1. 1.Data and Web Science GroupUniversität MannheimMannheimGermany

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