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Computing Utility from Weighted Description Logic Preference Formulas

  • Azzurra Ragone
  • Tommaso Di Noia
  • Francesco M. Donini
  • Eugenio Di Sciascio
  • Michael P. Wellman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5948)

Abstract

We propose a framework to compute the utility of a proposal w.r.t. a preference set in a negotiation process. In particular, we refer to preferences expressed as weighted formulas in a decidable fragment of First Order Logic (FOL). Although here we tailor our approach for Description Logics endowed with disjunction, all the results keep their validity in any decidable fragment of FOL. DLs offer expressivity advantages over propositional representations, and allow us to relax the often unrealistic assumption of additive independence among attributes. We provide suitable definitions of the problem and present algorithms to compute utility in our setting. We also study complexity issues of our approach and demonstrate its usefulness with a running example in a multiattribute negotiation scenario.

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References

  1. 1.
    Keeney, R.L., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. John Wiley & Sons, New York (1976)Google Scholar
  2. 2.
    Gonzales, C., Perny, P.: GAI networks for utility elicitation. In: Ninth Intl. Conf. on Principles of Knowledge Representation and Reasoning, Whistler, BC, Canada, pp. 224–234 (2004)Google Scholar
  3. 3.
    Bacchus, F., Grove, A.: Graphical models for preference and utility. In: Eleventh Conf. on Uncertainty in Artificial Intelligence, Montreal, pp. 3–10 (1995)Google Scholar
  4. 4.
    Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: A tool for representing and reasoning about conditional ceteris paribus preference statements. Journal of Artificial Intelligence Research 21, 135–191 (2004)zbMATHMathSciNetGoogle Scholar
  5. 5.
    Engel, Y., Wellman, M.P.: CUI networks: A graphical representation for conditional utility independence. Journal of Artificial Intelligence Research 31, 83–112 (2008)zbMATHMathSciNetGoogle Scholar
  6. 6.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge Univ. Press, Cambridge (2002)Google Scholar
  7. 7.
    Baader, F., Hanschke, P.: A Scheme for Integrating Concrete Domains into Concept Languages. Technical Report Tech. Rep. RR-91-10 (1991)Google Scholar
  8. 8.
    Pinkas, G.: Propositional non-monotonic reasoning and inconsistency in symmetric neural networks. In: Twelfth Intl. Joint Conf. on Artificial Intelligence, pp. 525–531 (1991)Google Scholar
  9. 9.
    Lafage, C., Lang, J.: Logical representation of preferences for group decision making. In: Seventh Intl. Conf. on Principles of Knowledge Representation and Reasoning, pp. 457–468 (2000)Google Scholar
  10. 10.
    Chevaleyre, Y., Endriss, U., Lang, J.: Expressive power of weighted propositional formulas for cardinal preference modelling. In: Tenth International Conference on Principles of Knowledge Representation and Reasoning, pp. 145–152 (2006)Google Scholar
  11. 11.
    Ragone, A., Di Noia, T., Di Sciascio, E., Donini, F.: A logic-based framework to compute Pareto agreements in one-shot bilateral negotiation. In: Seventeenth European Conference on Artificial Intelligence, pp. 230–234 (2006)Google Scholar
  12. 12.
    Ragone, A., Di Noia, T., Di Sciascio, E., Donini, F.M.: Description logics for multi-issue bilateral negotiation with incomplete information. In: Twenty-Second AAAI Conference on Artificial Intelligence, pp. 477–482 (2007)Google Scholar
  13. 13.
    Ragone, A., Di Noia, T., Di Sciascio, E., Donini, F.M.: Alternating-offers protocol for multi-issue bilateral negotiation in semantic-enabled marketplaces. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 395–408. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Lukasiewicz, T., Schellhase, J.: Variable-strength conditional preferences for matchmaking in description logics. In: KR, pp. 164–174 (2006)Google Scholar
  15. 15.
    Baader, F., Hollunder, B.: \(\mathcal{KRIS}\): \(\mathcal{K}\)nowledge \(\mathcal{R}\)epresentation and \(\mathcal{I}\)nference \(\mathcal{S}\)ystem. SIGART Bulletin 2(3), 8–14 (1991)CrossRefGoogle Scholar
  16. 16.
    Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Englewood Cliffs (1982)zbMATHGoogle Scholar
  17. 17.
    Fishburn, P.C.: Interdependence and additivity in multivariate, unidimensional expected utility theory. International Economic Review 8, 335–342 (1967)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Azzurra Ragone
    • 1
  • Tommaso Di Noia
    • 1
  • Francesco M. Donini
    • 2
  • Eugenio Di Sciascio
    • 1
  • Michael P. Wellman
    • 3
  1. 1.SisInfLabPolitecnico di BariBariItaly
  2. 2.Università della TusciaViterboItaly
  3. 3.Artificial Intelligence LaboratoryUniversity of MichiganAnn ArborUSA

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