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Answer Set Programming for Computing Decisions Under Uncertainty

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6717))

Abstract

Possibility theory offers a qualitative framework for modeling decision under uncertainty. In this setting, pessimistic and optimistic decision criteria have been formally justified. The computation by means of possibilistic logic inference of optimal decisions according to such criteria has been proposed. This paper presents an Answer Set Programming (ASP)-based methodology for modeling decision problems and computing optimal decisions in the sense of the possibilistic criteria. This is achieved by applying both a classic and a possibilistic ASP-based methodology in order to handle both a knowledge base pervaded with uncertainty and a prioritized preference base.

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References

  1. Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)

    Book  MATH  Google Scholar 

  2. Brewka, G.: Answer Sets and Qualitative Decision Making. Synthese 146(1-2), 171–187 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  3. Brewka, G.: Answer Sets and Qualitative Optimization. Logic Journal of the IGPL 14(3), 413–433 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  4. Brewka, G., Niemelä, I., Syrjänen, T.: Logic Programs with Ordered Disjunction. Computational Intelligence 20(2), 333–357 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  5. Brewka, G., Niemelä, I., Truszczynski, M.: Answer Set Optimization. In: Gottlob, G., Walsh, T. (eds.) 18th Int. Joint Conf. on Artificial Intelligence (IJCAI 2003), pp. 867–872. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

  6. Confalonieri, R., Nieves, J.C., Osorio, M., Vázquez-Salceda, J.: Possibilistic Semantics for Logic Programs with Ordered Disjunction. In: Link, S., Prade, H. (eds.) FoIKS 2010. LNCS, vol. 5956, pp. 133–152. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Confalonieri, R., Prade, H., Nieves, J.C.: Handling Exceptions in Logic Programming without Negation as Failure. Accepted in the 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (2011)

    Google Scholar 

  8. Dubois, D., Lang, J., Prade, H.: Possibilistic Logic. In: Handbook of Logic in Artificial Intelligence and Logic Programming, pp. 439–513. Oxford Univ. Press, Oxford (1994)

    Google Scholar 

  9. Dubois, D., Fargier, H., Prade, H., Perny, P.: Qualitative decision theory: from Savage’s axioms to nonmonotonic reasoning. J. ACM 49(4), 455–495 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  10. Dubois, D., Le Berre, D., Prade, H., Sabbadin, R.: Using Possibilistic Logic for Modeling Qualitative Decision: ATMS-based Algorithms. Fundamenta Informaticae 37(1-2), 1–30 (1999)

    MathSciNet  MATH  Google Scholar 

  11. Dubois, D., Prade, H.: Possibility theory as a basis for qualitative decision theory. In: 14th Int. Joint Conf. on Artificial Intelligence (IJCAI 1995), pp. 1924–1930. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  12. Dubois, D., Prade, H., Sabbadin, R.: Decision-theoretic foundations of qualitative possibility theory. Europ. Journal of Operational Research 128(3), 459–478 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  13. Dubois, D., Prade, H., Sabbadin, R.: A Possibilistic Logic Machinery for Qualitative Decision. In: AAAI Spring Symposium on Qualitative Preferences in Deliberation and Practical Reasoning, pp. 47–54. AAAI Press, Menlo Park (1997)

    Google Scholar 

  14. Grabos, R.: Qualitative model of decision making. In: Bussler, C.J., Fensel, D. (eds.) AIMSA 2004. LNCS (LNAI), vol. 3192, pp. 480–489. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  15. Nicolas, P., Garcia, L., Stéphan, I., Lefèvre, C.: Possibilistic uncertainty handling for answer set programming. Annals of Mathematics and Artificial Intelligence 47(1-2), 139–181 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  16. Sabbadin, R.: Decision As Abduction? In: Prade, H. (ed.) Proceedings of 13th European Conference on Artificial Intelligence, pp. 600–604. Wiley, Chichester (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, C/ Jordi Girona Salgado 1-3, E - 08034, Barcelona, Spain

    Roberto Confalonieri

  2. Institut de Recherche en Informatique Toulouse (IRIT), Universitè Paul Sabatier, 118 Route de Narbonne, 31062, Toulouse Cedex 9, France

    Henri Prade

Authors
  1. Roberto Confalonieri
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  2. Henri Prade
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Editor information

Editors and Affiliations

  1. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, BT9 5BN, Belfast, UK

    Weiru Liu

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Confalonieri, R., Prade, H. (2011). Answer Set Programming for Computing Decisions Under Uncertainty. In: Liu, W. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2011. Lecture Notes in Computer Science(), vol 6717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22152-1_41

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  • DOI: https://doi.org/10.1007/978-3-642-22152-1_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22151-4

  • Online ISBN: 978-3-642-22152-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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