Defeasible Logic

  • Donald Nute
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2543)


We often reach conclusions partially on the basis that we do not have evidence that the conclusion is false. A newspaper story warning that the local water supply has been contaminated would prevent a person from drinking water from the tap in her home. This suggests that the absence of such evidence contributes to her usual belief that her water is safe. On the other hand, if a reasonable person received a letter telling her that she had won a million dollars, she would consciously consider whether there was any evidence that the letter was a hoax or somehow misleading before making plans to spend the money. All to often we arrive at conclusions which we later retract when contrary evidence becomes available. The contrary evidence defeats our earlier reasoning. Much of our reasoning is defeasible in this way. Since around 1980, considerable research in AI has focused on how to model reasoning of this sort. In this paper, I describe one theoretical approach to this problem, discuss implementation of this approach as an extension of Prolog, and describe some application of this work to normative reasoning, learning, planning, and other types of automated reasoning.


Decision Support System Atomic Formula Precedence Relation Normative Reasoning Proof Theory 
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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  1. 1.
    Moore, R.: Semantical considerations on non-monotonic logic. Artificial Intelligence 25 (1985) 75–94zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Loui, R.: Theory and Computation of Uncertain Inference and Decision. PhD thesis, The University of Rochester (1987) Technical Report 228, Department of Computer Science.Google Scholar
  3. 3.
    Loui, R.: Defeat among arguments: A system of defeasible inference. Computational Intelligence 3 (1987) 100–106CrossRefGoogle Scholar
  4. 4.
    Geffner, H.: Default Reasoning: Causal and Conditional Theories. PhD thesis, UCLA (1989) Research Report 137, Cognitive Systems Laboratory, Department of Computer Science.Google Scholar
  5. 5.
    Geffner, H., Pearl, J.: A framework for reasoning with defaults. In Kyburg, H., Loui, R., Carlson, G., eds.: Knowledge Representation and Defeasible Reasoning. Studies in Cognitive Systems. Kluwer Academic Publishers, Boston (1989) 69–88Google Scholar
  6. 6.
    Pollock, J.: A theory of defeasible reasoning. International Journal of Intelligent Systems 6 (1991) 33–54CrossRefGoogle Scholar
  7. 7.
    Pollock, J.: Self-defeating argument. Minds and Machines 1 (1991) 367–392CrossRefGoogle Scholar
  8. 8.
    Nute, D.: Defeasible logic. In Gabbay, D., Hogger, C., eds.: Handbook of Logic for Artificial Intelligence and Logic Programming. Volume III. Oxford University Press, Oxford (1994)Google Scholar
  9. 9.
    Schurtz, G.: Defeasible reasoning based on constructive and cumulative rules. In Casati, R., Smith, B., White, G., eds.: Philosophy and Cognitive Sciences. Hölder-Pichler-Tempsky (1994) 297–310Google Scholar
  10. 10.
    Makinson, D.: On a fundamental problem of deontic logic. In Prakken, H., McNamara, P., eds.: ΔEON’98: 4th International Workshop on Deontic Logic in Computer Science, Università degli Studi di Bologna (1998)Google Scholar
  11. 11.
    Dung, P.M., Kowalski, R.A., Toni, F.: Synthesis of proof procedures for default reasoning. In: Proceedings of the international workshop on logic programming synthesis and transformation, Springer Lecture Notes on Computer Science 1207 (1996) 313–324Google Scholar
  12. 12.
    Reiter, R.: A logic for default reasoning. Artificial Intelligence 13 (1980) 81–132zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Konolige, K.: On the relation between default theories and autoepistemic logic. Artificial Intelligence 35 (1988) 343–382zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Morreau, M.: Reasons to think and act. In Nute, D., ed.: Defeasible Deontic Logic. Synthese Library. Kluwer Academic Publishers, Dordrecht, Netherlands (1997) 139–158Google Scholar
  15. 15.
    Donnelly, S.: Semantics, soundness, and incompleteness for a defeasible logic. Master’s thesis, Artificial Intelligence Center, The University of Georgia (1999)Google Scholar
  16. 16.
    Nute, D.: Apparent obligation. In Nute, D., ed.: Defeasible Deontic Logic. Synthese Library. Kluwer Academic Publishers, Dordrecht, Netherlands (1997) 287–315Google Scholar
  17. 17.
    Gabbay, D.: Labelled Deductive Systems. Volume 1. Oxford University Press, Oxford (1996)zbMATHGoogle Scholar
  18. 18.
    Makinson, D., Schechta, K.: Floating conclusions and zombie paths: two deep difficulties in the ‘directly skeptical’ approach to inheritance nets. Artificial Intelligence48 (1991) 199–209zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Nute, D.: Norms, priorities, and defeasibility. In McNamara, P., Prakken, H., eds.: Norms, Logics and Information Systems. IOS Press, Amsterdam (1999) 201–218.Google Scholar
  20. 20.
    Nute, D., Lewis, M.: A users manual for d-Prolog. Research Report 01-0016, Artificial Intelligence Programs, The University of Georgia (1986)Google Scholar
  21. 21.
    Nute, D.: Basic defeasible logic. In nas del Cerro, L.F., Penttonen, M., eds.: Intensional Logics for Programming. Oxford University Press (1992) 125–154Google Scholar
  22. 22.
    Nute, D.: A decidable quantified defeasible logic. In Prawitz, D., Skyrms, B., Westerstahl, D., eds.: Logic, Methodology and Philosophy of Science IX. Elsevier Science B. V, New York (1994) 263–284Google Scholar
  23. 23.
    Covington, M., Nute, D., Vellino, A.: Prolog Programming in Depth, Second Edition. Prentice-Hall, Englewood Cliffs, NJ (1997)zbMATHGoogle Scholar
  24. 24.
    Conklin, J., Begemena, M.L.: gIBIS: A tool for all reasons. Journal of the American Society for Information Systems 40 (1989) 140–152Google Scholar
  25. 25.
    Hua, H., Kimbrough, S.: On hypermedia-based argumentation decision support systems. unpublished manuscript (1995)Google Scholar
  26. 26.
    Nute, D., Henderson, C., Hunter, Z.: Defeasible logic graphs ii: Implementation. Decision Support Systems (to appear)Google Scholar
  27. 27.
    Nute, D., Erk, K.: Defeasible logic graphs i: Theory. Decision Support Systems (to appear)Google Scholar
  28. 28.
    Nute, D., Mann, R., Brewer, B.: Conrtolling expert system recommendations with defeasible logic. Decision Support Systems 6 (1990) 153–164CrossRefGoogle Scholar
  29. 29.
    Georgo., D.M., Murdick, R.G.: Manager’s guide to forecasting. Harvard Business Review (1986) 110–120Google Scholar
  30. 30.
    Puppa, A.: A comparison: Knowledge representation in Prolog and in defeasible Prolog. Master’s thesis, Artificial Intelligence Center, The University of Georgia (1997)Google Scholar
  31. 31.
    Nute, D.: V-World. Software, Artificial Intelligence Center, The University of Georgia (2001) Available online at
  32. 32.
    Hunter, Z.: dd-Prolog: A deontic extension of d-Prolog. Master’s thesis, The University of Georgia (1997)Google Scholar
  33. 33.
    Ryu, Y.: A Formal Representation of Normative Systems: A Defeasible Deontic Reasoning Approach. PhD thesis, University of Texas (1992)Google Scholar
  34. 34.
    Dhanesha, K.: Normative expert system using deontic logic and defeasible reasoning. Master’s thesis, The University of Georgia (1994)Google Scholar
  35. 35.
    Ryu, Y., Lee, R.: Defeasible deontic reasoning: A logic programming model. In Meyer, J.J.C., Wieringa, R.J., eds.: Deontic Logic in Computer Science: Normative System Speciffication. John Wiley & Sons Ltd. (1993)Google Scholar
  36. 36.
    Ryu, Y., Lee, R.: Defeasible deontic reasoning and its applications to normative systems. Decision Support Systems 4 (1995) 59–73CrossRefGoogle Scholar
  37. 37.
    Ryu, Y., Lee, R.: Deontic logic viewed as defeasible reasoning. In Nute, D., ed.: Defeasible Deontic Logic: Essays in Nonmonotonic Normative Reasoning. Kluwer Academic Publishers, Dordrecht, Holland (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Donald Nute
    • 1
  1. 1.Department of Philosophy and Artificial Intelligence CenterThe University of GeorgiaAthensUSA

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