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Document Type : Latin Dissertation
Language of Document : English
Record Number : 151430
Doc. No : ET23222
Main Entry : Jenngang Shih
Title Proper : SEQUENTIAL INSTANCE-BASED LEARNING FOR PLANNING IN THE CONTEXT OF AN IMPERFECT INFORMATION GAME
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : Finding sequential concepts, as in planning, is a complex task because of the exponentialsize of the search space. Empirical learning is an effective way to find sequential conceptsfrom observations. Sequential Instance-Based Learning (SIBL), which is presented here.is an empirical learning approach. modeled after instance-Based Learning (IBL), thatlearns sequential concepts, ordered sequences of state-action pairs to perform a synthesistask. SIBL is highly effective: it learns expert-level knowledge. SIBL demonstrates thefeasibility of using an empirical learning approach to discover sequential concepts. In ad-dition. this approach suggests a general framework that systematically extends empiricallearning to learning sequential concepts. In this dissertation. SLBL is tested on the domainof bridge.....-....,....-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject : Electericl tess
: برق
electronic file name : TL46458.pdf
Title and statement of responsibility and : SEQUENTIAL INSTANCE-BASED LEARNING FOR PLANNING IN THE CONTEXT OF AN IMPERFECT INFORMATION GAME [Thesis]
 
 
 
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