A Language for Default Reasoning about Actions

  • Hannes Strass
  • Michael Thielscher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7265)


Action languages allow for a concise representation of actions and their effects while at the same time being easily readable and writable for humans. In this paper, we introduce \({\mathcal D}\), the first action language that centres around default reasoning about actions and change. It allows to specify normal Reiter-style defaults and provides a semantics that has at its core the groundedness of conclusions. This is not only of use for default conclusions, but also for \({\mathcal D}\)’s solution to the ramification problem. Additionally, our language does not suffer from Yale Shooting-like counterexamples since it uses different mechanisms for default persistence and default change. The answer set programming paradigm is used as the basis of \({\mathcal D}\)’s implementation, which we prove sound and complete with respect to the language’s semantics. We finally present a showcase application for the language: its straightforward solution to the qualification problem.


Logic Program Action Sequence Logic Programming Action Language Frame Problem 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gelfond, M., Lifschitz, V.: Representing Action and Change by Logic Programs. Journal of Logic Programming 17(2/3&4), 301–321 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Gelfond, M., Lifschitz, V.: Action Languages. Electronic Transactions on Artificial Intelligence 3 (1998)Google Scholar
  3. 3.
    Giunchiglia, E., Lifschitz, V.: An Action Language Based on Causal Explanation: Preliminary Report. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI 1998), pp. 623–630. American Association for Artificial Intelligence, Menlo Park (1998)Google Scholar
  4. 4.
    Kakas, A., Miller, R.: A Simple Declarative Language for Describing Narratives with Actions. Journal of Logic Programming 31(1–3), 157–200 (1997); Reasoning about Actions and ChangeMathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N., Turner, H.: Nonmonotonic Causal Theories. Artificial Intelligence 153(1-2), 49–104 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Eiter, T., Faber, W., Leone, N., Pfeifer, G., Polleres, A.: A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity. ACM Transactions on Computational Logic 5, 206–263 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    McCarthy, J.: Epistemological Problems of Artificial Intelligence. In: Proceedings of the Fifth International Joint Conference on Artificial Intelligence (IJCAI 1977), pp. 1038–1044 (1977)Google Scholar
  8. 8.
    Reiter, R.: A Logic for Default Reasoning. Artificial Intelligence 13, 81–132 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Gelfond, M., Lifschitz, V.: Classical Negation in Logic Programs and Disjunctive Databases. New Generation Computing 9, 365–385 (1991)CrossRefzbMATHGoogle Scholar
  10. 10.
    Gelfond, M., Lifschitz, V., Rabinov, A.: What Are the Limitations of the Situation Calculus? In: Boyer, R.S., Pase, W. (eds.) Automated Reasoning. Automated Reasoning Series, vol. 1, pp. 167–179. Springer, Netherlands (1991)CrossRefGoogle Scholar
  11. 11.
    Thielscher, M.: Causality and the Qualification Problem. In: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning (KR), Cambridge, MA, pp. 51–62 (November 1996)Google Scholar
  12. 12.
    Baumann, R., Brewka, G., Strass, H., Thielscher, M., Zaslawski, V.: State Defaults and Ramifications in the Unifying Action Calculus. In: Proceedings of the Twelfth International Conference on the Principles of Knowledge Representation and Reasoning, Toronto, Canada, pp. 435–444 (May 2010)Google Scholar
  13. 13.
    Mueller, E.: Commonsense Reasoning. Morgan Kaufmann (2006)Google Scholar
  14. 14.
    Lakemeyer, G., Levesque, H.: A Semantical Account of Progression in the Presence of Defaults. In: Proceedings of the Twenty-first International Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 842–847 (2009)Google Scholar
  15. 15.
    Michael, L., Kakas, A.: A Unified Argumentation-Based Framework for Knowledge Qualification. In: Davis, E., Doherty, P., Erdem, E. (eds.) Proceedings Commonsense, Stanford, CA (March 2011)Google Scholar
  16. 16.
    Britz, K., Meyer, T., Varzinczak, I.: Preferential Reasoning for Modal Logics. Electronic Notes in Theoretical Computer Science 278, 55–69 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Kim, T.W., Lee, J., Palla, R.: Circumscriptive Event Calculus as Answer Set Programming. In: IJCAI, pp. 823–829 (July 2009)Google Scholar
  18. 18.
    Lee, J., Palla, R.: Situation Calculus as Answer Set Programming. In: Proceedings of the Twenty-Fourth Conference on Artificial Intelligence (AAAI 2010), pp. 309–314 (July 2010)Google Scholar
  19. 19.
    Casolary, M., Lee, J.: Representing the Language of the Causal Calculator in Answer Set Programming. In: Proceedings of the Twenty-Seventh International Conference on Logic Programming (ICLP 2011) (July 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hannes Strass
    • 1
  • Michael Thielscher
    • 2
  1. 1.Computer Science InstituteUniversity of LeipzigGermany
  2. 2.School of Computer Science and EngineeringThe University of New South WalesAustralia

Personalised recommendations