Engineering complete planning domain descriptions is often very costly because of human error or lack of domain knowledge. While many have studied knowledge acquisition, relatively few have studied the synthesis of plans when the domain model is incomplete (i.e., actions have incomplete preconditions or effects). Prior work has evaluated the correctness of plans synthesized by disregarding such incomplete features, but not how to synthesize plans by reasoning about the incompleteness. In this work, we describe several techniques for reasoning that takes into account action incompleteness to increase the number of interpretations under which the plans will succeed. Among the techniques, we show that representing explanations of plan failure with prime implicants provides a natural approach to comparing plans by counting prime implicants instead of models—leading to better scalability and comparable quality plans.
We present and empirically evaluate a forward heuristic search planner, called DeFAULT, that synthesizes plans by propagating information about faults due to incompleteness both within the state space and the relaxed planning space. We compare DeFAULT with a control planner that uses the fast forward (FF) heuristic (measuring plan length and ignoring incompleteness). The results show that DeFAULT i) scales comparable to the planner using the FF heuristic (while finding better solutions), and ii) scales better when counting prime implicants than models.
|Commitee:||Allan, Vicki H., Flann, Nicholas|
|School:||Utah State University|
|School Location:||United States -- Utah|
|Source:||MAI 51/04M(E), Masters Abstracts International|
|Subjects:||Artificial intelligence, Computer science|
|Keywords:||Automated planning, Domain independent, Incomplete domains|
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