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Towards a foundation for evaluating AI planners

Published online by Cambridge University Press:  27 February 2009

Nabil A. Kartam  and
David E. Wilkins
Nabil A. Kartam
Affiliation:
Department of Civil Engineering, The University of Maryland, College Park, MD 20742, U.S.A.
David E. Wilkins
Affiliation:
Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, U.S.A.
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Abstract

There exists a large body of Artificial Intelligence (AI) research on generating plans, i.e. linear or non-linear sequences of actions, to transform an initial world state to some desired goal state. However, much of the planning research to date has been complicated, ill-understood, and unclear. Only a few of the developers of these planners have provided a thorough description of their research products, and those descriptions that exist are usually unrealistically favorable since the range of applications for which the systems are tested is limited to those for which they were developed. As a result, it is difficult to evaluate these planners and to choose the best planner for a different domain. To make a planner applicable to different planning problems, it should be domain independent. However, one needs to know the circumstances under which a general planner works so that one can determine its suitability for a specific domain.

This paper presents criteria for evaluating AI planners; these criteria fall into three categories: (1) performance issues, (2) representational issues, and (3) communication issues. This paper also assesses four non-linear AI planners (NOAH, NONLIN, SIPE and TWEAK) based on a study of the published literature and on communication (via electronic mail, meetings and correspondence) with their developers.

Type
Research Article
Information
AI EDAM , Volume 4 , Issue 1 , February 1990 , pp. 1 - 13
Copyright
Copyright © Cambridge University Press 1990

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References

Chapman, D. 1987. Planning for conjunctive goals. Artificial Intelligence 32, 333–377.CrossRefGoogle Scholar
Currie, K. and Tate, A. 1988. O-PLAN: The Open Planning Architecture. University of Edinburgh: Artificial Intelligence Applications Institute.Google Scholar
Daniel, L. 1977. Planning: modifying non-linear plans. Edinburgh AI working paper 24, Edinburgh: Edinburgh University.Google Scholar
Dean, T., Firby, R. and Miller, D. 1988. Hierarchical planning involving deadlines, travel time, and resources. Computational Intelligence 4, 381–398.CrossRefGoogle Scholar
Drummond, M. and Currie, K. 1988. Exploiting temporal coherence in nonlinear plan construction. Computational Intelligence, 4, 341–348.CrossRefGoogle Scholar
Georgeff, M. P. 1987. Planning. Palo Alto: Annual Reviews.Google Scholar
Kartam, N. A. 1989. Investigating the Utility of Artificial Intelligence Techniques for Automatic Generation of Construction Project Plans. PhD Thesis, Dept. of Civil Engineering, Stanford University, CA.Google Scholar
Kartam, N. A., Levitt, R. E. and Wilkins, D. E. 1990. A centralized approach for representing and resolving interactions among multi-agent tasks while planning hierarchically. To appear in the Proceedings of the Sixth Conference on Artificial Intelligence Applications. Santa Barbara, CA: IEEEGoogle Scholar
Lifschitz, V. 1987. On the semantics of STRIPS. In: Georgeff, M. and Lansky, A. (Eds) Reasoning about Actions and Plans: Proceedings of the 1986 Workshop. San Mateo: Morgan Kaufmann.Google Scholar
Lloyd, J. W. 1984. Foundations of Logic Programming. New York: Springer-Verlag.CrossRefGoogle Scholar
Parsaye, K. and Chignell, M. 1988. Expert Systems for Experts. New York: John Wiley.Google Scholar
Paulson, B. C. Jr. 1972. Man-computer concepts for planning and scheduling. ASCE Journal of the Construction Division 98, 275–286.CrossRefGoogle Scholar
Sacerdoti, E. D. 1977. A Structure for Plans and Behaviour. New York: Elsevier.Google Scholar
Sussman, G. J. 1975. A Computer Model of Skill Acquisiton. New York: Elsevier.Google Scholar
Tate, A. 1976. Project Planning Using A Hierarchic Non-linear Planner. TR 25, Dept. of Artificial Intelligence, University of Edinburgh.Google Scholar
Tate, A. 1977. Generating project networks. IJCAl, 888–893.Google Scholar
Wilkins, D. E. 1988. Practical Planning: Extending the Classical AI Planning Paradigm. San Mateo: Morgan Kaufmann.Google Scholar
Wilkins, D. E. 1989. Can AI Planners Solve Practical Problems? TR 468, Artificial Intelligence Center, SRI International, Menlo Park, CA.Google Scholar