This is the first year of a three year continuing award. To plan in complex, large-scale tasks requires elaborate and costly knowledge engineering and programming to design and implement task-specific planners. General planning methods, on the other hand, apply only to small constrained tasks because of the inherent combinatiorial search. Although some worthy inroads have been made by modern planners, such as SIPE, to combine general planning methods with hand-crafted domain heuristic, This research considers a different alternative: focused machine learning of control knowledge to guide search. In particular, the fully-implemented PRODIGY planner presents the ideal substrate on which to investigate multiple learning methods. Previously explanation-based learning was successfully integrated in PRODIGY. This research addresses automated acquisition of abstraction spaces for hierarchical planning, case-based learning with derivational analogy, and their synergistic integration. Proper abstractions in partially-decomposable domains provide major performance improvements, and automated abstraction eliminates the bottleneck of laborious hand-coded abstraction. Derivational analogy provides and extremely flexible case-based replay mechanism, falling back to general planning when solution to subgoals do not transfer. Together, both methods should prove even more effective, with abstraction providing the key indices for case-based memory search (those features that cannot abstracted away, such as bottleneck points or roots of dependency networks), and derivational analogy speeding up search within abstraction spaces. Through this automated acquisition of control knowledge, planners will be able to solve increasingly complex planning domains.
