This thesis addresses the problem of generating goal-directed plans involving very many elementary actions. For example, to achieve a real-world goal such as earning a Ph.D., an intelligent agent may carry out millions of actions at the level of reading a word or striking a key. Given computational constraints, it seems that such long-term planning must incorporate reasoning with high-level actions (such as delivering a conference talk or typing a paragraph of a research paper) that abstract over the precise details of their implementations, despite the fact that these details must eventually be determined for the actions to be executed. This multi-level decision-making process is the subject of hierarchical planning.

To most effectively plan with high-level actions, one would like to be able to correctly identify whether a high-level plan works, without first considering its low-level implementations. The first contribution of this thesis is an "angelic" semantics for high-level actions that enables such inferences. This semantics also provides bounds on the costs of high-level plans, enabling the identification of provably high-quality (or even optimal) high-level solutions.

Effective hierarchical planning also requires algorithms to efficiently search through the space of high-level plans for high-quality solutions. We demonstrate how angelic bounds can be used to speed up search, and introduce a novel decomposed planning framework that leverages task-specific state abstraction to eliminate many redundant computations. These techniques are instantiated in the Decomposed, Angelic, State-abstracted, Hierarchical A* (DASH-A*) algorithm, which can find hierarchically optimal solutions exponentially faster than previous algorithms.




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