Abstract
A logic programming formalization of action domains has been well-studied and some work exists on the combination with learning methods. We extend previous work to deal with the indirect effects of actions and to solve the problems derived from cyclic dependences.
Keywords
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lorenzo, D.: Learning causal action theories from narratives of actions. In: Benferhat, S., Giunchiglia, E. (eds.) Proceedings of the 9th International Workshop on Non-monotonic Reasoning, pp. 349–355 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lorenzo, D. (2003). Learning Action Theories with Ramifications. In: Pires, F.M., Abreu, S. (eds) Progress in Artificial Intelligence. EPIA 2003. Lecture Notes in Computer Science(), vol 2902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24580-3_32
Download citation
DOI: https://doi.org/10.1007/978-3-540-24580-3_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20589-0
Online ISBN: 978-3-540-24580-3
eBook Packages: Springer Book Archive