ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
On the complexity of blocks-world planning
Artificial Intelligence
The computational complexity of propositional STRIPS planning
Artificial Intelligence
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Machine Learning
Encoding domain knowledge for prositional planning
Logic-based artificial intelligence
Learning Generalized Policies from Planning Examples Using Concept Languages
Applied Intelligence
The GRT planning system: backward heuristic construction in forward state-space planning
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Backbones in optimization and approximation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A meta-CSP model for optimal planning
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
A linear programming heuristic for optimal planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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This paper reports an analysis of near-optimal Blocks World planning. Various methods are clarified, and their time complexity is shown to be linear in the number of blocks, which improves their known complexity bounds. The speed of the implemented programs (ten thousand blocks are handled in a second) enables us to make empirical observations on large problems. These suggest that the above methods have very close average performance ratios, and yield a rough upper bound on those ratios well below the worst case of 2. Further, they lead to the conjecture that in the limit the simplest linear time algorithm could be just as good on average as the optimal one.