Abstract
Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperation has the potential of achieving better plans than either the human or the machine can create alone. Human planners often take a case-based approach to planning, relying on their past experience and planning by retrieving and adapting past planning cases. Planning by analogical reasoning in which generative and case-based planning are combined, as in Prodigy/Analogy, provides a suitable framework to study this mixed-initiative integration. However, having a human user engaged in this planning loop creates a variety of new research questions. The challenges we found creating a mixed-initiative planning system fall into three categories: planning paradigms differ in human and machine planning; visualization of the plan and planning process is a complex, but necessary task; and human users range across a spectrum of experience, both with respect to the planning domain and the underlying planning technology. This paper presents our approach to these three problems when designing an interface to incorporate a human into the process of planning by analogical reasoning with Prodigy/Analogy. The interface allows the user to follow both generative and case-based planning, it supports visualization of both plan and the planning rationale, and it addresses the variance in the experience of the user by allowing the user to control the presentation of information.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Blythe, J. & Veloso, M. M. (in press). Analogical Replay for Efficient Conditional Planning. To appear in the Proceedings of the Fourteenth National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press.
Blythe, J., Veloso, M. M., & de Souza, L. (1997). The Prodigy user interface (Tech. Rep. No. CMU-CS-97-114). Carnegie Mellon University, Computer Science Dept.
Carbonell, J. G. (1986), Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In R. Michalski, J. Carbonell & T. Mitchell (Eds.), Machine learning: An artificial intelligence approach, Vol. 2 (pp. 371–392). San Mateo, CA: Morgan Kaufmann.
Cox, M. T., & Veloso, M. M. (1997). Controlling for unexpected goals when planning in a mixed-initiative setting. Submitted.
Ferguson, G., Allen, J. F., & Miller, B. (1996). TRAINS-95: Towards a mixed-initiative planning assistant. In Proceedings of the Third International Conference on AI Planning Systems (AIPS-96), Edinburgh, Scotland, May 29–31,1996.
Fink, E., & Veloso, M. M. (1996). Formalizing the Prodigy planning algorithm. In M. Gahllab & A. Milani (Eds.), New Directions in AI Planning (pp. 261–271). Amsterdam: IOS Press
Hammond, K. J. (1989). Case-based planning: Viewing planning as a memory task. Vol. 1. of Perspectives in artificial intelligence. San Diego, CA: Academic Press.
Kolodner, J. L. (1993). Case-based reasoning. San Mateo, CA: Morgan Kaufmann.
Oates, T. & Cohen, P. R. (1994). Toward a plan steering agent: Experiments with schedule maintenance. In Proceedings of the Second International Conference on Planning Systems (AIPS-94) (pp. 134–139). Menlo Park, CA: AAAI Press.
Riesbeck, C. K., & Schank, R. C. (1989). Inside case-based reasoning (pp. 1–24). Hillsdale, NJ: Lawrence Erlbaum Associates.
Seifert, C. M., Hammond, K. J., Johnson, H. M., Converse, T. M., McDougal, T. F., & Vanderstoep, S. W. (1994). Case-based learning: Predictive features in indexing. Machine Learning, 16, 37–56.
Veloso, M. (1994). Planning and learning by analogical reasoning. Springer-Verlag.
Veloso, M. (1997). Merge strategies for multiple case plan replay. This volume.
Veloso, M., & Carbonell, J. G. (1993). Towards scaling up machine learning: A case study with derivational analogy in PRODIGY (pp. 233–272). In S. Minton (Ed.), Machine learning methods for planning. Morgan Kaufmann.
Veloso, M., Carbonell, J. G., Perez, A., Borrajo, D., Fink, E., & Blythe, J. (1995). Integrating planning and learning: The PRODIGY architecture. Journal of Theoretical and Experimental Artificial Intelligence, 7(1), 81–120.
Veloso, M. M, Mulvehill, A., & Cox, M. T. (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.
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). Supporting combined human and machine planning: An interface for planning by analogical reasoning. In: Leake, D.B., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 1997. Lecture Notes in Computer Science, vol 1266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63233-6_522
Download citation
DOI: https://doi.org/10.1007/3-540-63233-6_522
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63233-7
Online ISBN: 978-3-540-69238-6
eBook Packages: Springer Book Archive