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Learning Partially Observable, Deterministic Action Models (II)

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Title: Learning Partially Observable, Deterministic Action Models (II)
Author(s): Chang, Allan; Amir, Eyal
Subject(s): algorithms machine learning
Abstract: We present new algorithms for learning a logical model of actions' effects and preconditions in partially observable domains. The algorithms maintain a logical representation of the set of possible action models after each observation and action execution. The algorithms perform learning in unconditional STRIPS action domains, which represent a new class of action models that can be learned tractably. Unlike previous algorithms, these algorithms are capable of learning preconditions or learning in the presence of action failures. The algorithms take time and space polynomial in the number of domain features, and can maintain a representation that stays indefinitely compact.
Issue Date: 2005-11
Genre: Technical Report
Type: Text
URI: http://hdl.handle.net/2142/11125
Other Identifier(s): UIUCDCS-R-2005-2661
Rights Information: You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS: 2009-04-20
 

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