Planning for conjunctive goals
Artificial Intelligence
ACM Transactions on Database Systems (TODS)
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Supporting collaborative planning: the plan integration problem
Supporting collaborative planning: the plan integration problem
A model of planning for plan efficiency: taking advantage of operator overlap
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Using partial global plans to coordinate distributed problem solvers
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
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Merging operators in a plan can yield significant savings in the cost to execute a plan. Past research in planning has concentrated on handling harmful interactions among plans, but the understanding of positive ones has remained at a qualitative, heuristic level. This paper provides a quantitative study for plan optimization and presents both optimal and approximate algorithms for finding minimum-cost merged plans. With worst and average case complexity analysis and empirical tests, we demonstrate that efficient and wellbehaved approximation algorithms are applicable for optimizing general plans with large sizes.