Abstract
The event calculus is a formalism for reasoning about actions and change in dynamic systems. It has been used in diverse areas including planning and communications protocol specification. Writing event calculus programs requires the construction of domain specific axioms (DSAs) - a programming task which is non-trivial, and one that hinders the broader use of the event calculus. This work demostrates that such axioms can be learned from temporal observations using Inductive Logic programming (ILP) techniques, in particular theory c0ompletion. The theory of logical back-propagation as a mechanism for theory completion is described and its implementation in the ILP system Progol is used here. These techniques were used to investigate learning DSAS for the traditional AI blocks world. In the experiments Progol, utilising logical back-propagation, learned correct DSAs. These results provide encouragement and highlight the possibility of discovering causal relationships from data in temporal databases, and also learning the domain specific knowledge necessary in the development of plans.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Abeysinghe, G. K.: Event Calculus to Support Temporal Reasoning in a Clinical Domain, Ph.D. thesis, University of Southampton (1993).
Bratko, L., Muggleton, S., Varsek, A.: Learning Qualitative Models of Dynamic Systems, in Inductive Logic Programming, S. Muggleton, Editor. (1992) Academic Press: London. p. 437–452.
De Raedt, L.: Interactive Theory Revision: An Inductive Logic Programming Approach. (1992), London-Academic Press.
Denecker, M., et al.: A Realistic Experiment in Knowledge Representation in Open Event Calculus: Protocol Specification in Proceedings of JICSLP'96, the Joint International Conference and Symposium on Logic Programming. (1996) Bonn, Germany: MIT Press.
Kakas, A. C., Miller, R. S.: A Simple Declarative Language for Describing Narratives with Actions. Journal of Logic Programming: Special Issue on Reasoning about Action and Change (1997).
Kowalski, R., Sergot, M.: A Logic-based Calculus of Events. New Generation Computing 4 (1986) 67–95.
Moyle, S., Muggleton, S.: Experiments in Learning Event Calculus Programs, Oxford University Computing Laboratory PRG-TR-23-97 (1997).
Muggleton, S.: Inverse Entailment and Progol. New Generation Computing 13(3 and 4) (1995) 245–286.
Sablon, G.: Iterative Versionspaces with an application in Inductive Logic Programming, Ph.D. thesis, Katholic University of Leuven (1995).
Shanahan, M.: Solving the Frame Problem: A Mathematical Investigation of the Common Sense Law of Inertia. (1997), MIT Press.
Srinivasan, A., Camacho, R.: Numerical reasoning in ILP in Machine Intelligence 15. (1995), Oxford.
Sripada, S. M.: Efficient Implementation of the Event Calculus for Temporal Database Applications in Proceedings of the 12th International Conference on Logic Programming. (1995), Japan.
Stickel, M. E.: A Prolog technology theorem prover: a new exposition and implementation in Prolog. Theoretical Computer Science 104(1) (1992) 109–128.
Author information
Authors and Affiliations
Corresponding author
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moyle, S., Muggleton, S. (1997). Learning programs in the event calculus. In: Lavrač, N., Džeroski, S. (eds) Inductive Logic Programming. ILP 1997. Lecture Notes in Computer Science, vol 1297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3540635149_49
Download citation
DOI: https://doi.org/10.1007/3540635149_49
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63514-7
Online ISBN: 978-3-540-69587-5
eBook Packages: Springer Book Archive