Planning as search: a quantitative approach
Artificial Intelligence
Automatically generating abstractions for problem solving
Automatically generating abstractions for problem solving
Automatically generating abstractions for planning
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Planning and Control in Artificial Intelligence: A Unifying Perspective
Applied Intelligence
SHOP: simple hierarchical ordered planner
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
HPGP: an abstraction-based framework for decision-theoretic planning
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
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This article presents a hierarchical planner to actuate in uncertain domains named HIPU – Hierarchical Planner under Uncertainty. The uncertainties treated by HIPU include the probabilistic distribution of the operators effects and a distribution of probabilities on the possible initial states of the domain. HIPU automatically creates the abstraction hierarchy that will be used during planning, and for this it uses an extension of the Alpine method, adapted to act under uncertainty conditions. The planning process in HIPU happens initially in the highest level of abstraction, and the solution found in this level is refined by lower levels, until reaching the lowest level. During the search the plan evaluation is carried out, indicating if the plan achieves the goal with a success probability larger or equal to a previously defined value. To evaluate this probability, the planner uses the forward projection method.