Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Least-cost flaw repair: a plan refinement strategy for partial-order planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
The use of condition types to restrict search in an AI planner
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Common Lisp Analytical Statistics Packag: User Manual
Common Lisp Analytical Statistics Packag: User Manual
Test Case Generation as an AI Planning Problem
Automated Software Engineering
Accelerating partial-order planners: some techniques for effective search control and pruning
Journal of Artificial Intelligence Research
Flaw selection strategies for partial-order planning
Journal of Artificial Intelligence Research
A critical assessment of benchmark comparison in planning
Journal of Artificial Intelligence Research
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The search space in partial-order planning grows quickly with the number of subgoals and initial conditions, as well as less countable factors such as operator ordering and subgoal in teractions. For partial-order planners to solve more than simple problems, the expansion of the search space will need to be controlled. This paper presents four new approaches to controlling search space expansion by exploiting commonalities in emerging plans. These approaches are described in terms of their algorithms, their effect on the completeness and correctness of the underlying planner and their expected performance. The four new and two existing approaches are compared on several metrics of search space and planning overhead.