Planning as Refinement Search: A unified framework for comparative analysis of Search Space Size and Performance

In spite of the long history of classical planning, there has been very little comparative analysis of the search space characteristics of the multitude of existing planning algorithms. This has seriously inhibited efforts to fruitfully intergrate various approaches. In this paper we show that viewing planning as a general refinement search provides a unified framework for comparing the search spaces of various planning strategies, and in predicting their performance. We will provide a generic refinement search algorithm for planning, and show that all planners that search in the space of plans are special cases of this algorithm. In this process, we will provide a rational reconstruction of main ideas of refinement planning algorithms. We will then develop a model for estimating search space size of a refinement planner, and use this model to analyze a variety of tradeoffs between search space size, refinement cost and performance in refinement planning. This research is supported in part by National Science Foundation under grant IRI-9210997, and ARPA/Rome Laboratory planning initiative under grant F30602-93-C-0039.