TY - JOUR
T1 - A validation-structure-based theory of plan modification and reuse
AU - Kambhampati, Subbarao
AU - Hendler, James A.
N1 - Funding Information:
Bulk of this research was done while the first author was a graduate research assistant at the Center for Automation Research, University of Maryland, College Park. Lindley Darden and Larry Davis have significantly influenced the early development of this work. Mark Drummond, Amy Lansky, Jack Mostow, Austin Tate, David Wilkins, and the reviewers of IJCAI-89, AAA1-90 have provided several helpful comments on previous drafts. The paper also benefited from the extensive comments from one of the AI Journal referees. Dr. Kambhampati has been supported in part by the Defense Advanced Research Projects Agency and the U.S. Army Engineer Topographic Laboratories under contract DACA76-88-C-0008 (to the University of Maryland Center for Automation Research), the Office of Naval Research under contract N00014-88-K-0620 (to Stanford University Center for Design Research), and the Washington D.C. Chapter of A.C.M. through the "1988 Samuel N. Alexander A.C.M. Doctoral Fellowship Grant". Dr. Hendler is also affiliated with the UM Systems Research Center (an NSF supported engineering research center) and the UM Institute for Advanced Studies, and support for this research comes from ONR grant N00014-88-K-0560 and NSF grant IR1-8907890.
PY - 1992/6
Y1 - 1992/6
N2 - The ability to modify existing plans to accommodate a variety of externally imposed constraints (such as changes in the problem specification, the expected world state, or the structure of the plan) is a valuable tool for improving efficiency of planning by avoiding repetition of planning effort. In this paper, we present a theory of incremental plan modification suitable for hierarchical nonlinear planning, and describe its implementation in a system called PRIAR. In this theory, the causal and teleological structure of the plans generated by a planner are represented in the form of an explanation of correctness called the "validation structure". Individual planning decisions are justified in terms of their relation to the validation structure. Plan modification is formalized as a process of removing inconsistencies in the validation structure of a plan when it is being reused in a new or changed planning situation. The repair of these inconsistencies involves removing unnecessary parts of the plan and adding new nonprimitive tasks to the plan to establish missing or failing validations. The result is a partially reduced plan with a consistent validation structure, which is then sent to the planner for complete reduction. We discuss this theory, present an empirical evaluation of the resulting plan modification system, and characterize the coverage, efficiency and limitations of the approach.
AB - The ability to modify existing plans to accommodate a variety of externally imposed constraints (such as changes in the problem specification, the expected world state, or the structure of the plan) is a valuable tool for improving efficiency of planning by avoiding repetition of planning effort. In this paper, we present a theory of incremental plan modification suitable for hierarchical nonlinear planning, and describe its implementation in a system called PRIAR. In this theory, the causal and teleological structure of the plans generated by a planner are represented in the form of an explanation of correctness called the "validation structure". Individual planning decisions are justified in terms of their relation to the validation structure. Plan modification is formalized as a process of removing inconsistencies in the validation structure of a plan when it is being reused in a new or changed planning situation. The repair of these inconsistencies involves removing unnecessary parts of the plan and adding new nonprimitive tasks to the plan to establish missing or failing validations. The result is a partially reduced plan with a consistent validation structure, which is then sent to the planner for complete reduction. We discuss this theory, present an empirical evaluation of the resulting plan modification system, and characterize the coverage, efficiency and limitations of the approach.
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U2 - 10.1016/0004-3702(92)90056-4
DO - 10.1016/0004-3702(92)90056-4
M3 - Article
AN - SCOPUS:0026880070
VL - 55
SP - 193
EP - 258
JO - Artificial Intelligence
JF - Artificial Intelligence
SN - 0004-3702
IS - 2-3
ER -