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Constructing Simplified Plans via Truth Criteria ApproximationThis paper has presented an approach to dealing with the complexity of explanation-based learning plans in complex domains. This approach uses a simplified algorithm to construct plans, and employs later refinements to repair bugs in constructed plans. This algorithm has the theoretical properties of completeness and convergence upon soundness. This incremental reasoning planning and learning algorithm has been implemented using a partial-order constraint posting planner and empirically compared to a conventional exhaustive reasoning partial-order constraint-posting planner and learning algorithm. This comparison showed that 1) incremental reasoning significantly reduced learning costs compared to exhaustive reasoning, 2) Explanation-based Learning (EBL) reduced failures from incremental reasoning, 3) EBL with incremental reasoning required less search to solve problems than EBL with exhaustive reasoning.
Document ID
20060038308
Acquisition Source
Jet Propulsion Laboratory
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Authors
Chien, S.
DeJong, G.
Date Acquired
August 23, 2013
Publication Date
June 13, 1994
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Distribution Limits
Public
Copyright
Other
Keywords
Explanation-based learning EBL incremental reasoning planning artificial
intelligence simplified plans

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