Planning as search: a quantitative approach
Artificial Intelligence
Explanation-based learning: a problem solving perspective
Artificial Intelligence
A Critical Look at Experimental Evaluations of EBL
Machine Learning
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
A critical look at Koblock's hierarchy mechanism
Proceedings of the first international conference on Artificial intelligence planning systems
Learning by analogical reasoning in general problem-solving
Learning by analogical reasoning in general problem-solving
Statistical Methods for Analyzing Speedup Learning Experiments
Machine Learning
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This paper provides a systematic analysis of the relative utility of basing EBG based plan reuse techniques in partial ordering vs. total ordering planning frameworks. We separate the potential advantages into those related to storage compaction, and those related to the ability to exploit stored plans. We observe that the storage compactions provided by partially ordered partially instantiated plans can, to a large ex1ent, be exploited regardless of the underlying planner. We argue that it is in the ability to exploit stored plans during planning that partial ordering planners have some distinct advantages. In particular, to be able to flexibly reuse and extend the retrieved plans, a planner needs the ability to arbitrarily and efficiently "splice in" new steps and sub-plans into the retrieved plan. This is where partial ordering planners, with their least-commitment strategy, and flexible plan representations, score significantly over state-based planners as well as planners that search in the space of totally ordered plans. We will clarify and supporll this hypothesis through an empirical study of three planners and two reuse strategies.