Abstract
A mixed-initiative setting is where both human and machine are intimately involved in the planning process. We have identified a number of challenges that occur for traditional planning frameworks as humans are allowed more latitude. In this paper we will examine the types of unexpected goals that may be given to the underlying planning system and thereby how humans change the way planning must be performed. Users may want to achieve goals in terms of actions as well as states, they may specify goals that vary along a dimension of abstraction and specificity, and they may mix both top-level goals and subgoals when describing what they want a plan to do. We show how the Prodigy planning system has met these challenges when integrated with a force deployment tool called format and describe what opportunities this poses for a generative planning framework.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Carbonell, J. G.; Blythe, J.; Etzioni, O.; Gil, Y; Joseph, R.; Kahn, D.; Knoblock, C.; Minton, S.; Pérez, A.; Reilly, S.; Veloso, M. M.; and Wang, X. 1992. PRODIGY4.0: The Manual and Tutorial, Tech. Rep., CMU-CS-92-150, Deptartment of Computer Science, Carnegie Mellon University.
Fink, E, and Yang, Q. in press. Automatically Selecting and Using Primary Effects in Planning: Theory and Experiments. Artificial Intelligence.
Hayes-Roth, B., and Hayes-Roth, F. 1979. A Cognitive Model of Planning. Cognitive Science 3: 275–310.
McDermott, D. 1978. Planning and Acting. Cognitive Science 2: 71–109.
Myers, K. L. 1996. Strategic Advice for Hierarchical Planners. In Proceedings of the 5th International Conference on Principles of Knowledge Representation and Reasoning, 112–123. San Francisco: Morgan Kaufmann.
Mulvehill, A. 1996. Building, Remembering, and Revising Force Deployment Plans, In A. Tate ed. Advanced Planning Technology, 201–205. Menlo Park, CA: AAAI Press.
Mulvehill, A., and Christey, S. 1995. ForMAT — a Force Management and Analysis Tool. Bedford, MA: MITRE Corporation.
Pollack, M. E. 1990. Plans as Complex Mental Attitudes. In P. R. Cohen, J. Morgan and M. E. Pollack eds. Intentions in Communication, 77–104. Cambridge,MA: MIT Press.
Veloso, M. M. 1994. Planning and Learning by Analogical Reasoning. New York: Springer-Verlag.
Veloso, M. M. 1996. Towards Mixed-Initiative Rationale-Supported Planning. In A. Tate ed. Advanced Planning Technology, 277–282. Menlo Park, CA: AAAI Press.
Veloso, M.; Carbonell, J.; Pérez, A.; Borrajo, D.; Fink, E.; and Blythe, J. 1995. Integrating Planning and Learning: The PRODIGY Architecture. Journal of Experimental and Theoretical Artificial Intelligence 7(1): 81–120.
Veloso, M.; Mulvehill, A.; and Cox, M. in press. Rationale-Supported Mixed-Initiative Case-based Planning. To appear in Proceedings of the Ninth Annual Conference on Innovative Applications of Artificial Intelligence. Menlo Park, CA: AAAI Press.
Wilkins, D., and Desimone, R. 1994. Applying an AI Planner to Military Operations Planning. In M. Zweben and M. Fox eds. Intelligent Scheduling, 685–709. San Mateo, CA: Morgan Kaufmann.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cox, M.T., Veloso, M.M. (1997). Controlling for unexpected goals when planning in a mixed-initiative setting. In: Coasta, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 1997. Lecture Notes in Computer Science, vol 1323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0023933
Download citation
DOI: https://doi.org/10.1007/BFb0023933
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63586-4
Online ISBN: 978-3-540-69605-6
eBook Packages: Springer Book Archive