Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
O-Plan: the open planning architecture
Artificial Intelligence
Partial-order planning: evaluating possible efficiency gains
Artificial Intelligence
Intelligent scheduling
Derivation replay for partial-order planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
A comparative analysis of partial order planning and task reduction planning
ACM SIGART Bulletin
Artificial Intelligence - Special volume on planning and scheduling
Machine Learning Methods for Planning
Machine Learning Methods for Planning
Total-order and partial-order planning: a comparative analysis
Journal of Artificial Intelligence Research
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Constraint Solving for Proof Planning
Journal of Automated Reasoning
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Most current-day AI planning systems operate by iteratively refining a partial plan until it meets the goal requirements. In the past five years, significant progress has been made in our understanding of the spectrum and capabilities of such refinement planners. In this talk, I will summarize this understanding in terms of a unified framework for refinement planning and discuss several current research directions.