Transferring Learned Search Heuristics Investigators
نویسندگان
چکیده
منابع مشابه
Transferring Learned Control-Knowledge between Planners
As any other problem solving task that employs search, AI Planning needs heuristics to efficiently guide the problem-space exploration. Machine learning (ML) provides several techniques for automatically acquiring those heuristics. Usually, a planner solves a problem, and a ML technique generates knowledge from the search episode in terms of complete plans (macro-operators or cases), or heurist...
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A common goal for transfer learning research is to show that a learner can solve a source task and then leverage the learned knowledge to solve a target task faster than if it had learned the target task directly. A more difficult goal is to reduce the total training time so that learning the source task and target task is faster than learning only the target task. This paper addresses the seco...
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This paper presents a study on the transfer of learned control knowledge between two different planning techniques. We automatically learn heuristics (usually, in planning, heuristics are also named control knowledge) from one planner search process and apply them to a different planner. The goal is to improve this second planner efficiency solving new problems, i.e. to reduce computer resource...
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We investigate learning heuristics for domainspecific planning. Prior work framed learning a heuristic as an ordinary regression problem. However, in a greedy best-first search, the ordering of states induced by a heuristic is more indicative of the resulting planner’s performance than mean squared error. Thus, we instead frame learning a heuristic as a learning to rank problem which we solve u...
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Suboptimal search algorithms offer shorter solving times by sacrificing guaranteed solution optimality. While optimal search algorithms like A* and IDA* require admissible heuristics, suboptimal search algorithms need not constrain their guidance in this way. Previous work has explored using off-line training to transform admissible heuristics into more effective inadmissible ones. In this pape...
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