HMM-BASED SEMANTIC LEARNING FOR A MOBILE ROBOT

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" HMM-BASED SEMANTIC LEARNING FOR A MOBILE ROBOT "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	150514
Doc. No	:	ET22306
Main Entry	:	KEVIN MICHAEL SQUIRE
Title Proper	:	HMM-BASED SEMANTIC LEARNING FOR A MOBILE ROBOT
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	We are developing a intelligent robot and attempting to teach it language. While there are manyaspects of this research, for the purposes of this dissertation the most important are the followingideas. Language is primarily based on semantics, not syntax, which is the focus in speech recogni-tion research these days. To truly learn meaning, a language engine cannot simply be a computerprogram running on a desktop computer analyzing speech. It must be part of a more general,embodied intelligent system, one capable of using associative learning to form concepts from theperception of experiences in the world, and further capable of manipulating those concepts symboli-cally. This dissertation explores the use of hidden Markov models (HMMs) in this capacity. HMMsare capable of automatically learning and extracting the underlying structure of continuous-valuedinputs and representing that structure in the states of the model. These states can then be treatedas symbolic representations of the inputs. We show how a model consisting of a cascade of HMMscan be embedded in a small mobile-...,..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	:	TL45512.pdf
Title and statement of responsibility and	:	HMM-BASED SEMANTIC LEARNING FOR A MOBILE ROBOT [Thesis]