Abstract
To date, no one planner has demonstrated clearly superior performance. Although researchers have hypothesized that this should be the case, no one has performed a large study to test its limits. In this research, we tested performance of a set of planners to determine which is best on what types of problems. The study included six planners and over 200 problems. We found that performance, as measured by number of problems solved and computation time, varied with no one planner solving all the problems or being consistently fastest. Analysis of the data also showed that most planners either fail or succeed quickly and that performance depends at least in part on some easily observable problem/domain features. Based on these results, we implemented a meta-planner that interleaves execution of six planners on a problem until one of them solves it. The control strategy for ordering the planners and allocating time is derived from the performance study data. We found that our meta-planner is able to solve more problems than any single planner, but at the expense of computation time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Barrett, A., Christianson, D., Friedman, M., Golden, K., Penberthy, S., Sun, Y., Weld, D.: UCPOP user’s manual. Technical Report TR 93-09-06d, Dept of Computer Science and Engineering, University of Washington, Seattle, WA, Version 4.0 (November 1996)
Blum, A., Furst, M.: Fast planning through planning graph analysis. Artificial Intelligence 90, 281–300 (1997)
Fink, E.: How to solve it automatically: Selection among problem-solving methods. In: Proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems (June 1998)
Howe, A.E., von Mayrhauser, A., Mraz, R.T.: Test case generation as an AI planning problem. Automated Software Engineering 4(1) (1997)
Kambhampati, S.: Challenges in bridging plan synthesis paradigms. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (1997)
Kambhampati, S., Srivastava, B.: Universal Classical Planning: An algorithm for unifying state-space and plan-space planning. In: Current Trends in AI Planning: EWSP 1995. IOS Press, Amsterdam (1995)
Kautz, H., Selman, B.: Blackbox: A new approach to the application of theorem proving to problem solving. In: Working notes of the AIPS 1998 Workshop on Planning as Combinatorial Search, Pittsburgh, PA (1998)
Koehler, J., Nebel, B., Hoffmann, J., Dimopoulos, Y.: Extending planning graphs to an ADL subset. In: Fourth European Conference in Planning (1997)
McDermott, D.: Aips98 planning competition results (June 1998), http://ftp.cs.yale.edu/pub/mcdermott/aipscomp-results.html
McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., Wilkins, D.: The Planning Domain Definition Language (May 1998)
Fox, M., Long, D.: The automatic inference of state invariants in TIM. JAIR 9, 367–421 (1998)
Simon, H.A., Kadane, J.B.: Optimal problems-solving search: All-or-none solutions. Artificial Intelligence 6, 235–247 (1975)
UCPOP Group. The UCPOP planner (1997), http://www.cs.washington.edu/research/projects/ai/www/ucpop.html
Veloso, M., Blythe, J.: Linkability: Examining causal link commitments in partial-order planning. In: Proceedings of the Second International Conference on AI Planning Systems (June 1994)
Veloso, M.M., Carbonell, J., Pérez, M.A., Borrajo, D., Fink, E., Blythe, J.: Integrating planning and learning: The prodigy architecture. Journal of Experimental and Theoretical Artificial Intelligence 7(1), 81–120 (1995)
Weld, D., Anderson, C., Smith, D.: Extending graphplan to handle uncertainty and sensing actions. In: Proc. of 16th National Conference on AI (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Howe, A.E., Dahlman, E., Hansen, C., Scheetz, M., von Mayrhauser, A. (2000). Exploiting Competitive Planner Performance. In: Biundo, S., Fox, M. (eds) Recent Advances in AI Planning. ECP 1999. Lecture Notes in Computer Science(), vol 1809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720246_5
Download citation
DOI: https://doi.org/10.1007/10720246_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67866-3
Online ISBN: 978-3-540-44657-6
eBook Packages: Springer Book Archive