This paper describes a new planner, HiPOP (Hierarchical Partial-Order Planner), which is domain-configurable and uses POP techniques to create hierarchical time-flexible plans. HiPOP takes as inputs a description of a domain, a problem, and some optional user-defined search-control knowledge. This additional knowledge takes the form of a set of abstract actions with optional methods to achieve them. HiPOP uses this knowledge to enrich the output by providing a hierarchical time-flexible partial-order plan that follows the given methods. We show in this paper how to use this additional knowledge in a POP algorithm and provide results on a domain with a strong hierarchy of actions. We compare our approach with other temporal planners on this domain.
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