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- W2184671750 abstract "The AI planning literature divides into two main paradigms, one focused on knowledge-lean methods that use only domain operators and another focused on knowledge-rich techniques that utilize hierarchical task networks or similar structures. In this paper, we present a unified framework that supports both approaches to planning, along with an implemented system that embodies its tenets. We demonstrate, on a variety of domains, that the system can handle problems stated in terms of goals, tasks, or their combination, and that it can generate plans using only operators, using hierarchical methods that decompose the problems entirely, or using an incomplete set of hierarchical methods that must be combined during planning. We report experiments that compare the efficiency of the system under these different conditions and the role of method filtering in making search tractable. In closing, we discuss other work on unifying the two paradigms and propose directions for future research." @default.
- W2184671750 created "2016-06-24" @default.
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- W2184671750 date "2015-01-01" @default.
- W2184671750 modified "2023-09-27" @default.
- W2184671750 title "A Unified Framework for Knowledge-Lean and Knowledge-Rich Planning" @default.
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