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Information fusion in logic: A brief overview

  • Laurence Cholvy
  • Anthony Hunter
Invited Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1244)

Abstract

Information fusion is the process of deriving a single consistent knowledgebase from multiple knowledgebases. This process is important in many cognitive tasks such as decision-making, planning, design, and specification, that can involve collecting information from a number of potentially conflicting perspectives or sources, or participants. In this brief overview, we focus on the problem of inconsistencies arising in information fusion. In the following, we consider reasoning with inconsistencies, acting on inconsistencies, and resolving inconsistencies.

Keywords

Modal Logic Classical Logic Belief Revision Information Fusion Deontic Logic 
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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References

  1. [AB75]
    A Anderson and N Belnap. Entailment: The Logic of Relevance and Necessity. Princeton University Press, 1975.Google Scholar
  2. [AGM85]
    C Alchourrón, P Gardenfors, and D Makinson. On the logic of theory change: Partial meet functions for contraction and revision. Journal of Symbolic Logic, 50:513–530, 1985.Google Scholar
  3. [Arr77]
    A Arruda. On the imaginary logic of NA Vasilev. In A Arruda, N Da Costa, and R Chuaqui, editors, Non-classical logics, model theory and computability. North Holland, 1977.Google Scholar
  4. [Bat80]
    D Batens. Paraconsistent extensional prepositional logics. Logique et Analyse, 90–91:195–234, 1980.Google Scholar
  5. [BCD+93]
    S Benferhat, C Cayrol, D Dubois, J Lang, and H Prade. Inconsistency management and prioritized syntax-based entailment. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1993.Google Scholar
  6. [BDP93]
    S Benferhat, D Dubois, and H Prade. Argumentative inference in uncertain and inconsistent knowledge bases. In Proceedings of Uncertainty in Artificial Intelligence, pages 1449–1445. Morgan Kaufmann, 1993.Google Scholar
  7. [BDP95]
    S Benferhat, D Dubois, and H Prade. A logical approach to reasoning under inconsistency in stratified knowledge bases. In Symbolic and Quantitative Approaches to Reasoning and Uncertainty, volume 956 of Lecture Notes in Computer Science, pages 36–43. Springer, 1995.Google Scholar
  8. [Bel77]
    N Belnap. A useful four-valued logic. In G Epstein, editor, Modern Uses of Multiple-valued Logic, pages 8–37. Reidel, 1977.Google Scholar
  9. [Bes89]
    Ph Besnard. An Introduction to Default Logic. Springer, 1989.Google Scholar
  10. [Bes91]
    Ph Besnard. Paraconsistent logic approach to knowledge representation. In M de Glas M and D Gabbay D, editors, Proceedings of the First World Conference on Fundamentals of Artificial Intelligence, pages 107–114. Angkor, 1991.Google Scholar
  11. [BH95]
    Ph Besnard and A Hunter. Quasi-classical logic: Non-trivializable classical reasoning from inconsistent information. In C Froidevaux and J Kohlas, editors, Symbolic and Quantitative Approaches to Uncertainty, volume 946 of Lecture Notes in Computer Science, pages 44–51, 1995.Google Scholar
  12. [BH97]
    Ph Besnard and A Hunter. Introduction to actual and potential contradictions. In Handbook of Defeasible Reasoning and Uncertainty Management, volume 3. Kluwer, 1997.Google Scholar
  13. [BKMS91]
    C. Baral, S. Kraus, J. Minker, and V.S. Subrahmanian. Combining multiple knowledge bases. IEEE Trans. on Knowledge and Data Engineering, 3(2), 1991.Google Scholar
  14. [BKMS92]
    C Baral, S Kraus, J Minker, and V Subrahmanian. Combining knowledge-bases of first-order theories. Computational Intelligence, 8:45–71, 1992.Google Scholar
  15. [Bre89]
    G Brewka. Preferred subtheories: An extended logical framework for default reasoning. In Proceedings of the Eleventh International Conference on Artificial Intelligence, pages 1043–1048, 1989.Google Scholar
  16. [Bre91]
    G Brewka. Common-sense Reasoning. Cambridge University Press, 1991.Google Scholar
  17. [CC95]
    L. Cholvy and F. Cuppens. Solving normative conflicts by merging roles. In Proceedings of the fifth International Conference on Artificial Intelligence and Law, Washington, May 1995.Google Scholar
  18. [CD89]
    F. Cuppens and R. Demolombe. How to recognize interesting topics to provide cooperative answering. Information Systems, 14(2), 1989.Google Scholar
  19. [CD92]
    S. Cazalens and R. Demolombe. Intelligent access to data and knowledge bases via users' topics of interest. In Proceedings of IFIP Conference, pages 245–251, 1992.Google Scholar
  20. [CD94]
    L. Cholvy and R. Demolombe. Reasoning with information sources ordered by topics. In Proceedings of Artificial Intelligence: Methods, Systems and Applications (AIMSA). World Scientific, Sofia, September 1994.Google Scholar
  21. [CFM91]
    W Carnielli, L Farinas, and M Marques. Contextual negations and reasoning with contradictions. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'91), 1991.Google Scholar
  22. [Cho93]
    L. Cholvy. Proving theorems in a multi-sources environment. In Proceedings of IJCAI, pages 66–71, 1993.Google Scholar
  23. [Cho94]
    L. Cholvy. A logical approach to multi-sources reasoning. In Proceedings of the Applied Logic Conference, number 808 in Lecture notes in Artificial Intelligence. Springer-Verlag, 1994.Google Scholar
  24. [Cho95]
    L. Cholvy. Automated reasoning with merged contradictory information whose reliability depends on topics. In Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU), Fribourg, July 1995.Google Scholar
  25. [Cho97]
    L. Cholvy. Reasoning about merged information. In Handbook of Defeasible Reasoning and Uncertainty Management, volume 1. Kluwer, 1997.Google Scholar
  26. [dC74]
    N C da Costa. On the theory of inconsistent formal systems. Notre Dame Journal of Formal Logic, 15:497–510, 1974.Google Scholar
  27. [DLP92]
    D. Dubois, J. Lang, and H. Prade. Dealing with multi-source information in possibilistic logic. In Proceedings of ECAI, pages 38–42, 1992.Google Scholar
  28. [Doy79]
    J Doyle. A truth maintenance system. Artificial Intelligence, 12:231–272, 1979.Google Scholar
  29. [DP97]
    D Dubois and H Prade. Handbook of Defeasible Reasoning and Uncertainty Management, volume 1. Kluwer, 1997.Google Scholar
  30. [EGH95]
    M Elvang-Goranssonand A Hunter. Argumentative logics: Reasoning from classically inconsistent information. Data and Knowledge Engineering Journal, 16:125–145, 1995.Google Scholar
  31. [FGH+94]
    A Finkelstein, D Gabbay, A Hunter, J Kramer, and B Nuseibeh. Inconsistency handling in multi-perspective specifications. IEEE Transactions on Software Engineering, 20(8): 569–578, 1994.Google Scholar
  32. [FH86]
    L. Farinas and A. Herzig. Reasoning about database updates. In Jack Minker, editor, Workshop of Foundations of deductive databases and logic programming, 1986.Google Scholar
  33. [FH92]
    L. Farinas and A. Herzig. Revisions, updates and interference. In A. Fuhrmann and Rott H, editors, Proceedings of the Konstanz colloquium in logic and information (LogIn-92. DeGruyter Publishers, 1992.Google Scholar
  34. [FH94]
    L. Farinas and A. Herzig. Interference logic=conditional logic + frame axiom. International JOurnal of Intelligent Systems, 9(1):119–130, 1994.Google Scholar
  35. [FKUV86]
    R Fagin, G Kuper, J Ullman, and M Vardi. Updating logical databases. Advances in Computing Research, 3:1–18, 1986.Google Scholar
  36. [Gar88]
    P Gardenfors. Knowledge in Flux: Modelling the Dynamics of Epistemic States. MIT Press, 1988.Google Scholar
  37. [GH91]
    D Gabbay and A Hunter. Making inconsistency respectable 1: A logical framework for inconsistency in reasoning. In Fundamentals of Artificial Intelligence, volume 535 of Lecture Notes in Computer Science, pages 19–32. Springer, 1991.Google Scholar
  38. [GH93]
    D Gabbay and A Hunter. Making inconsistency respectable 2: Meta-level handling of inconsistent data. In Symbolic and Qualitative Approaches to Reasoning and Uncertainty (ECSQARU'93), volume 747 of Lecture Notes in Computer Science, pages 129–136. Springer, 1993.Google Scholar
  39. [GH97]
    D Gabbay and A Hunter. Negation and contradiction. In What is negation? Kluwer, 1997.Google Scholar
  40. [GHR94]
    D Gabbay, C Hogger, and J Robinson. Handbook of Artificial Intelligence and Logic Programming, volume 3. Oxford University Press, 1994.Google Scholar
  41. [Gra91]
    G. Grahne. A modal analysis of subjonctive queries. In R. demolombe, L. farinas, and T. Imielinski, editors, Workshop on nonstandard queries and answers, Toulouse, 1991.Google Scholar
  42. [HN97]
    A Hunter and B Nuseibeh. Analysing inconsistent specifications. In Proceedings of 3rd International Symposium on Requirements Engineering, pages 78–86. IEEE Computer Society Press, 1997.Google Scholar
  43. [Hun96]
    A Hunter. Intelligent text handling using default logic. In Proceedings of the Eighth IEEE International Conference on Tools with Artificial Intelligence (TAI'96), pages 34–40. IEEE Computer Society Press, 1996.Google Scholar
  44. [Hun97]
    A Hunter. Paraconsistent logics. In Handbook of Defeasible Reasoning and Uncertainty Management. Kluwer, 1997.Google Scholar
  45. [Kle86]
    J De Kleer. An assumption-based TMS. Artificial Intelligence, 28:127–162, 1986.Google Scholar
  46. [KM89]
    H Katsuno and A Medelzon. A unified view of prepositional knowledgebase updates. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, 1989.Google Scholar
  47. [Lin87]
    F Lin. Reasoning in the presence of inconsistency. In Proceedings of the National Conference on Artificial Intelligence (AAAI'87), 1987.Google Scholar
  48. [Lin94]
    J Lin. A logic for reasoning consistently in the presence of inconsistency. In Proceedings of the Fifth Conference on Theoretical Aspects of Reasoning about Knowledge. Morgan Kaufmann, 1994.Google Scholar
  49. [Mot93]
    A. Motro. A formal framework for integrating inconsistent answers from multiple information sources. Technical Report ISSE-TR-93-106, George Mason University, 1993.Google Scholar
  50. [MR70]
    R Manor and N Rescher. On inferences from inconsistent information. Theory and Decision, 1:179–219, 1970.Google Scholar
  51. [MS88]
    J Martins and S Shapiro. A model of belief revision. Artificial Intelligence, 35:25–79, 1988.Google Scholar
  52. [MvdH97]
    J Meyer and W van der Hoek. Modal logics for representing incoherent knowledge. In Handbook of Defeasible Reasoning and Uncertainty Management, Volume 3. Kluwer, 1997.Google Scholar
  53. [Poo85]
    D Poole. A logical framework for default reasoning. Artificial Intelligence, 36:27–47, 1985.Google Scholar
  54. [PR84]
    G Priest and R Routley. Introduction: Paraconsistent logics. Studia Logica, 43:3–16, 1984.Google Scholar
  55. [Pra93]
    H Prakken. An argument framework for default reasoning. In Annals of mathematics and artificial intelligence, volume 9, 1993.Google Scholar
  56. [PRN88]
    G Priest, R Routley, and J Norman. Paraconsistent logic. Philosophia, 1988.Google Scholar
  57. [PS95]
    H. Prakken and G. Sartor. On the relation between legal language and legal argument: assumptions, applicability and dynamic priorities. In Proc. Fifth Conference on Artificial Intelligence and Law, University of Maryland, May, 1995.Google Scholar
  58. [PS96]
    H. Prakken and G. Sartor. A system for defeasible argumentation with defeasible prorities. In Proc. of FA PR'96, May, 1996.Google Scholar
  59. [RD96]
    L. Royakkers and F. Dignum. Defeasible reasoning with legal rules. In Proc of DEON'96, Sesimbra. Springer, 1996.Google Scholar
  60. [Roo93]
    N Roos. A logic for reasoning with inconsistent knowledge. Artificial Intelligence, 57(1):69–104, 1993.Google Scholar
  61. [Rya92]
    M Ryan. Representing defaults as sentences with reduced priority. In Principles of Knowledge Representation and Reasoning: Proceedings of the Third International Conference. Morgan Kaufmann, 1992.Google Scholar
  62. [Som94]
    Leá Sombé. Revision and updating in knowledgebases. Wiley, 1994.Google Scholar
  63. [TdT95]
    Y Tan and L Van der Torre. Why defeasible deontic logic needs a multi preference semantics. In Ch. froidevaux and J. Kohlas, editors, Quantitative and Qualitative Approches to Reasoning and Uncertainty, number 946 in Lectures notes in Artificial Intelligence. Springer, 1995.Google Scholar
  64. [Wag91]
    G Wagner. Ex contradictione nihil sequitur. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI91), 1991.Google Scholar
  65. [Win90]
    M Winslett. Updating logical databases. Cambridge University Press, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Laurence Cholvy
    • 1
  • Anthony Hunter
    • 2
  1. 1.ONERA-CERTToulouseFrance
  2. 2.Department of Computer ScienceUniversity College LondonLondonUK

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