Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Case-based planning: an integrated theory of planning, learning and memory
Case-based planning: an integrated theory of planning, learning and memory
Machine Learning and Its Applications, Advanced Lectures
Failure recovery: a model and experiments
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Analyzing failure recovery to improve planner design
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
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A persistent problem in machine planning is that of repairing plans that fail. Two solutions have been suggested to deal with this problem: planning critics and met a-planning techniques. Unfortunately, both of these suggestions suffer from lack of flexibility due to an extremely restricted view of how to describe planning failures. This paper presents an alternative approach in which plan failures are described in terms of causal explanations of why they occurred. These explanations are used to access different abstract replanning strategies, which are then turned into specific changes to the faulty plans. The approach is demonstrated using examples from CHEF, a case-based planner that creates and debugs plans in the domain of Szee hwan cooking.