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Document Type : Latin Dissertation
Language of Document : English
Record Number : 149595
Doc. No : ET21387
Main Entry : Patrick Paulson
Title Proper : USING REINFORCEMENT LEARNING FOR SIMILARITY ASSESSMENT IN CASE-BASED SYSTEMS
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : This research adds reinforcement learning to the similarity assessmentcomponent of case-based reasoning systems (CBR). The argument is thatreinforcement learning allows case-based reasoning to be more easily used inknowledge-poor domains.The system uses a case base composed of cases that were previouslyencountered. Each case suggests a solution to be tried in a particular context. Inan application, the system is presented with the context for a new problemsituation. The system finds a similar context within its case base and returns thesuggested solution. The user then provides the system with a reinforcement signalwhich indicates how well the solution performs in the new context. Given thisfeedback, the system modifies how.
Subject : Electericl tess
: برق
electronic file name : TL44546.pdf
Title and statement of responsibility and : USING REINFORCEMENT LEARNING FOR SIMILARITY ASSESSMENT IN CASE-BASED SYSTEMS [Thesis]
 
 
 
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