Planning as Refinement Search: A Unified Framework for Evaluating Design Tradeoffs in Partial-Order Planning (1995)
| Venue: | Artificial Intelligence |
| Citations: | 101 - 22 self |
BibTeX
@ARTICLE{Kambhampati95planningas,
author = {Subbarao Kambhampati and Craig A. Knoblock and Qiang Yang},
title = {Planning as Refinement Search: A Unified Framework for Evaluating Design Tradeoffs in Partial-Order Planning},
journal = {Artificial Intelligence},
year = {1995},
volume = {76},
pages = {167--238}
}
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Abstract
Despite the long history of classical planning, there has been very little comparative analysis of the performance tradeoffs offered by the multitude of existing planning algorithms. This is partly due to the many different vocabularies within which planning algorithms are usually expressed. In this paper we show that refinement search provides a unifying framework within which various planning algorithms can be cast and compared. Specifically, we will develop refinement search semantics for planning, provide a generalized algorithm for refinement planning, and show that planners that search in the space of (partial) plans are specific instantiations of this algorithm. The different design choices in partial order planning correspond to the different ways of instantiating the generalized algorithm. We will analyze how these choices affect the search-space size and refinement cost of the resultant planner, and show that in most cases they trade one for the other. Finally, we will concentrate on two specific design choices, viz., protection strategies and tractability refinements, and develop some hypotheses regarding the effect of these choices on the performance on practical problems. We will support these hypotheses with a series of focused empirical studies.
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