Temporal Extensions to Defeasible Logic

  • Guido Governatori
  • Paolo Terenziani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4830)


In this paper, we extend Defeasible Logic (a computationally-oriented non-monotonic logic) in order to deal with temporalised rules. In particular, we extend the logic to cope with durative facts, as well as with delays between the antecedent and the consequent of rules. We showed that the extended temporalised framework is suitable to model different types of causal relations which have been identified by the specialised literature. We also prove that the computational properties of the original logic are still retained by the extended approach.


Temporal Constraint Strict Rule Causal Reasoning Temporal Extension Strong Rule 
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.
    Allen, J.F: Towards a general theory of action and time. Artificial Intelligence 23, 123–154 (1984)zbMATHCrossRefGoogle Scholar
  2. 2.
    Antoniou, G.: Nonmonotonic rule system on top of ontology layer. In: Bergmann, R. (ed.) Experience Management. LNCS (LNAI), vol. 2432, pp. 394–398. Springer, Heidelberg (2002)Google Scholar
  3. 3.
    Antoniou, G., Billington, D., Governatori, G., Maher, M.J.: Representation results for defeasible logic. ACM Transactions on Computational Logic 2(2), 255–287 (2001)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Antoniou, G., Billington, D., Governatori, G., Maher, M.J., Rock, A.: A family of defeasible reasoning logics and its implementation. In: Proc. ECAI 2000, pp. 459–463 (2000)Google Scholar
  5. 5.
    Dechter, R., Meiri, I., Pearl, J.: Temporal constraint networks. Artificial Intelligence 49, 61–95 (1991)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Governatori, G.: Representing business contracts in RuleML. International Journal of Cooperative Information Systems 14(2-3), 181–216 (2005)CrossRefGoogle Scholar
  7. 7.
    Governatori, G., Dumas, M., ter Hofstede, A.H.M., Oaks, P.: A formal approach to protocols and strategies for (legal) negotiation. In: Proc. ICAIL 2001, pp. 168–177 (2001)Google Scholar
  8. 8.
    Governatori, G., Hulstijn, J., Riveret, R., Rotolo, A.: Characterising deadlines in temporal modal defeasible logic. In: Orgun, M.A., Thorton, J. (eds.) Australian AI 2007. LNCS(LNAI), vol. 4830, pp. 480–496. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    Governatori, G., Padmanabhan, V., Antonino, R.: Rule-based agents in temporalised defeasible logic. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 31–40. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Governatori, G., Rotolo, A.: Defeasible logic: Agency, intention and obligation. In: Lomuscio, A.R., Nute, D. (eds.) DEON 2004. LNCS (LNAI), vol. 3065, pp. 114–128. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Governatori, G., Rotolo, A., Padmanabhan, V.: The cost of social agents. In: Proc. AAMAS 2006, pp. 513–520 (2006)Google Scholar
  12. 12.
    Governatori, G., Rotolo, A., Sartor, G.: Temporalised normative positions in defeasible logic. In: Proc ICAIL 2005, pp. 25–34 (2005)Google Scholar
  13. 13.
    Grosof, B.N.: Representing e-commerce rules via situated courteous logic programs in RuleML. Electronic Commerce Research and Applications 3(1), 2–20 (2004)CrossRefGoogle Scholar
  14. 14.
    Guha, R.V., Lenat, D.B.: Building Large Knowledge Based Systems. Addison-Wesley, Reading (1991)Google Scholar
  15. 15.
    Hamlet, I., Hunter, J.: Representation of time in medical expert systems. LNMI 33, 112–119 (1987)Google Scholar
  16. 16.
    Konolidge, K.: Using default and causal reasoning in diagnosis. Annals of Mathematics and Artificial Intelligence 11, 97–135 (1994)CrossRefGoogle Scholar
  17. 17.
    Maher, M.J.: Propositional defeasible logic has linear complexity. Theory and Practice of Logic Programming 1(6), 691–711 (2001)zbMATHMathSciNetCrossRefGoogle Scholar
  18. 18.
    Maher, M.J., Rock, A., Antoniou, G., Billington, D., Miller, T.: Efficient defeasible reasoning systems. International Journal of Artificial Intelligence Tools 10(4), 483–501 (2001)CrossRefGoogle Scholar
  19. 19.
    McDermott, D.: A temporal logic for reasoning about processes and plans. Cognitive Science 6, 101–105 (1982)CrossRefGoogle Scholar
  20. 20.
    Nute, D.: Defeasible logic. In: Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 3, pp. 353–395. Oxford University Press, Oxford (1994)Google Scholar
  21. 21.
    Rieger, C., Grinberg, M.: The declarative representation and simulation of causality in physical mechanisms. In: Proc. IJCAI 1977, pp. 250–256 (1977)Google Scholar
  22. 22.
    Sandewall, E.: Features and Fluents. Oxford Scientific Publications, Oxford (1994)zbMATHGoogle Scholar
  23. 23.
    Shoham, Y.: Reasoning About Change. MIT Press, Cambridge (1987)Google Scholar
  24. 24.
    Sosa, E.: Causation and Conditionals. Oxford university Press, Oxford (1975)Google Scholar
  25. 25.
    Terenziani, P., Torasso, P.: Time, action-types and causation: an integrated analysis. Computaional Intelligence 11(3), 529–552 (1995)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Guido Governatori
    • 1
  • Paolo Terenziani
    • 2
  1. 1.School of ITEE, The University of Queensland, BrisbaneAustralia
  2. 2.Università del Piemonte Orientale, AlessandriaItaly

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