Abstract
The Qualification Problem arises for planning agents in realworld environments, where unexpected circumstances may at any time prevent the successful performance of an action. We present a logic programming method to cope with the Qualification Problem in the action programming language Flux, which builds on the Fluent Calculus as a solution to the fundamental Frame Problem. Our system allows to plan under the default assumption that actions succeed as they normally do, and to reason about these assumptions in order to recover from unexpected action failures.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Reference
Gerhard Brewka. Adding priorities and specificity to default logic. In C. MacNish, D. Pearce, and L. M. Pereira, editors, Proc. of the European Workshop on Logics in AI (JELIA), volume 838 of LNAI, pages 247–260, York, UK, September 1994. Springer.
Patrick Doherty, Joakim Gustafsson, Lars Karlsson, and Jonas Kvarnström. Temporal action logics (TAL): Language specification and tutorial. Electronic Transactions on Artificial Intelligence, 2(3-4):273–306, 1998. http://www.ep.liu.se/ ea/cis/1998/015/.
Richard E. Fikes and Nils J. Nilsson. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2:189–208, 1971.
Thom Frühwirth. Theory and practice of constraint handling rules. Journal of Logic Programming, 37(1-3):95–138, 1998.
Giuseppe De Giacomo, Ray Reiter, and Mikhail Soutchanski. Execution monitoring of high-level robot programs. In Cohn, Schubert, and Shapiro, editors, Proc. of the International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 453–464, Trento, Italy, June 1998.
Hector Levesque and Maurice Pagnucco. Legolog: Inexpensive experiments in cognitive robotics. In Cognitive Robotics Workshop at ECAI, pages 104–109, Berlin, Germany, August 2000.
Hector Levesque, Fiora Pirri, and Ray Reiter. Foundations for a calculus of situations. Electronic Transactions on Artificial Intelligence, 3(1-2):159–178, 1998. http://www.ep.liu.se/ea/cis/1998/018/.
Hector J. Levesque, Raymond Reiter, Yves Lespérance, Fangzhen Lin, and Richard B. Scherl. GOLOG: A logic programming language for dynamic domains. Journal of Logic Programming, 31(1-3):59–83, 1997.
Vladimir Lifschitz. Formal theories of action (preliminary report). In J. McDermott, editor, Proc. of IJCAI, pages 966–972, Milan, Italy, August 1987. Morgan Kaufmann.
John W. Lloyd. Foundations of Logic Programming. Series Symbolic Computation. Springer, second, extended edition, 1987.
Yves Martin. Solving the Qualification Problem in FLUX. Master’s thesis, TU Dresden, Germany, March 2001. http://www.cl.inf.tu-dresden.de/~yves.
Norman McCain and Hudson Turner. Satisfiability planning with causal theories. In A. G. Cohn, L. K. Schubert, and S. C. Shapiro, editors, Proc. of the International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 212–223, Trento, Italy, June 1998. Morgan Kaufmann.
John McCarthy. Epistemological problems of artificial intelligence. In Proc. of IJCAI, pages 1038–1044, Cambridge, MA, 1977. MIT Press.
John McCarthy. Applications of circumscription to formalizing common-sense knowledge. Artificial Intelligence, 28:89–116, 1986.
Ray Reiter. A logic for default reasoning. Artificial Intelligence, 13:81–132, 1980.
Murray Shanahan. Event calculus planning revisited. In Proc. of the European Conference on Planning (ECP), volume 1348 of LNAI, pages 390–402. Springer, 1997.
Michael Thielscher. Ramification and causality. Artificial Intelligence, 89(1-2):317–364, 1997.
Michael Thielscher. Introduction to the Fluent Calculus. Electronic Transactions on Artificial Intelligence, 2(3-4):179–192, 1998. http://www.ep.liu.se/ea/ cis/1998/014/.
Michael Thielscher. From Situation Calculus to Fluent Calculus: State update axioms as a solution to the inferential frame problem. Artificial Intelligence, 111(1-2):277–299, 1999.
Michael Thielscher. The fluent calculus: A specification language for robots with sensors in nondeterministic, concurrent, and ramifying environments. Technical Report CL-2000-01, Artificial Intelligence Institute, Department of Computer Science, Dresden University of Technology, 2000.
Michael Thielscher. The qualification problem: A solution to the problem of anomalous models. Artificial Intelligence, 2001.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martin, Y., Thielscher, M. (2001). Addressing the Qualification Problem in FLUX. In: Baader, F., Brewka, G., Eiter, T. (eds) KI 2001: Advances in Artificial Intelligence. KI 2001. Lecture Notes in Computer Science(), vol 2174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45422-5_21
Download citation
DOI: https://doi.org/10.1007/3-540-45422-5_21
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42612-7
Online ISBN: 978-3-540-45422-9
eBook Packages: Springer Book Archive