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Possibilistic Reasoning in Multi-Context Systems: Preliminary Report

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7458)

Abstract

This paper makes the first attempt to establish a framework for possibilistic reasoning in (nonmonotonic) multi-context systems, called possibilistic MCS. We first introduce the syntax for possibilistic MCS and then define its equilibrium semantics based on Brewka and Eiter’s nonmonotonic multi-context systems. Then we investigate several properties and develop a fixoint theory for possibilistic MCS.

Keywords

  • Logic Program
  • Belief State
  • Possibility Distribution
  • Possibilistic Reasoning
  • Possibility Degree

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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  • DOI: 10.1007/978-3-642-32695-0_18
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References

  1. Benferhat, S., Sossai, C.: Reasoning with multiple-source information in a possibilistic logic framework. Information Fusion 7, 80–96 (2006)

    CrossRef  Google Scholar 

  2. Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing 6(2), 161–180 (2010)

    CrossRef  Google Scholar 

  3. Bikakis, A., Patkos, T., Antoniou, G., Plexousakis, D.: A Survey of Semantics-Based Approaches for Context Reasoning in Ambient Intelligence. In: Mühlhäuser, M., Ferscha, A., Aitenbichler, E. (eds.) AmI 2007 Workshops. CCIS, vol. 11, pp. 14–23. Springer, Heidelberg (2008)

    Google Scholar 

  4. Brewka, G., Eiter, T.: Equilibria in heterogeneous nonmonotonic multi-context systems. In: Proc. AAAI, pp. 385–390 (2007)

    Google Scholar 

  5. Brewka, G., Roelofsen, F., Serafini, L.: Contextual default reasoning. In: Proc. IJCAI, pp. 268–273 (2007)

    Google Scholar 

  6. Dubois, D., Lang, J., Prade, H.: Possibilistic logic. In: Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 3, pp. 439–513 (1995)

    Google Scholar 

  7. Serafini, L., Giunchiglia, F.: Multilanguage hierarchical logics, or: how we can do without modal logics. In: Artificial Intelligence, pp. 29–70 (1994)

    Google Scholar 

  8. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proc. 5th ICLP, pp. 1070–1080 (1988)

    Google Scholar 

  9. Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, New York (1987)

    MATH  CrossRef  Google Scholar 

  10. McCarthy, J.: Notes on formalizing context. In: Proc. IJCAI, pp. 555–560 (1993)

    Google Scholar 

  11. Nicolas, P., Garcia, L., Stéphan, I., Lefèvre, C.: Possibilistic uncertainty handling for answer set programming. In: Annals of Mathematics and Artificial Intelligence, pp. 139–181 (2006)

    Google Scholar 

  12. Roelofsen, F., Serafini, L.: Minimal and absent information in contexts. In: Proc. IJCAI, pp. 558–563 (2005)

    Google Scholar 

  13. Yager, R.R.: An introduction to applications of possibility theory. Human Syst. Manag., 246–269 (1983)

    Google Scholar 

  14. Zadeh L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 3–28 (1978)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Jin, Y., Wang, K., Wen, L. (2012). Possibilistic Reasoning in Multi-Context Systems: Preliminary Report. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_18

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  • DOI: https://doi.org/10.1007/978-3-642-32695-0_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32694-3

  • Online ISBN: 978-3-642-32695-0

  • eBook Packages: Computer ScienceComputer Science (R0)