Abstract
The paper presents an approach for reasoning about potential causality in plans authored directly by humans in a mixed initiative framework. The approach uses only the temporal ordering of the actions and the task structure of the plan. The term potential is used to emphasize the uncertainty in the causal ordering since no requirement is made on the existence of a complete domain theory as in standard partial order planning. The core contribution of the paper is a formalization and algorithm for extracting a parsimonious description of a potential causality relation, which is presented to the modeler as a representation of the candidate space of sets of causal links consistent with the authored plan. The paper also discusses an implemented system based on this algorithm, and its application in the context of execution.
The research reported here was supported in part by the Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory under contract No. F30602-00-C-0038. The views and conclusions contained herein are those of the author and should not be interpreted as representing the official policy or endorsements, either expressed or implied, of any of the above organizations or any person connected with them.
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.
References
Weld, D.: Recent advances in AI planning. AI Magazine 20, 93–123 (1999)
Burstein, M., McDermott, D.: Issues in the development of human-computer mixedinitiative planning. In: Gorayska, B., Mey, J.L. (eds.) Cognitive Technology, pp. 285–303. Elsevier, Amsterdam (1996)
Kambhampati, S., Kedar, S.: A unified framework for explanation-based generalization of partially ordered and partially instantiated plans. Artificial Intelligence 67, 29–70 (1994)
Veloso, M., Carbonell, J.: Derivational analogy in prodigy: Automating case acquisition, storage and utilization. Machine Learning, 249–278 (1993)
Veloso, M.: Towards mixed-initiative rationale-supported planning. In: Tate, A. (ed.) Advanced Planning Technology: Technological Achievements of the ARPA/Rome Laboratory Planning Initiative, pp. 277–282. AAAI Press, Menlo Park (1996)
Myers, K.L.: Toward a theory of qualitative reasoning about plans. Technical report, A.I. Center, SRI (2001)
Veloso, M., Perez, M., Carbonell, J.: Nonlinear planning with parallel resource allocation. In: Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling and Control, pp. 207–212 (1990)
Regnier, P., Fade, B.: Complete determination of parallel actions and temporal optimization in linear plans of action. In: Hertzberg, J. (ed.) EWSP 1991. LNCS (LNAI), vol. 522, pp. 100–111. Springer, Heidelberg (1991)
Bäckström, C.: Finding Least Constrained Plans and Optimal Parallel Executions is Harderthan We Thought. In: Proceedings of the Second European Workshop on Planning (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
El Fattah, Y. (2004). Potential Causality in Mixed Initiative Planning. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_71
Download citation
DOI: https://doi.org/10.1007/978-3-540-24677-0_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22007-7
Online ISBN: 978-3-540-24677-0
eBook Packages: Springer Book Archive