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Patrick H. Winston. Miller, Catherine (Catherine T.) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2007-03-12T17:52:26Z 2007-03-12T17:52:26Z 2006 2006 http://hdl.handle.net/1721.1/36773 78927507 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. Includes bibliographical references (leaf 43). If we are ever to have intelligent systems, they will need memory. Memory is the core of learning; intelligence is about entering, extracting, and synthesizing its contents. What makes the memory problem difficult is that memory is not a simple collection of facts. The how and why of where those facts were acquired is a key part of how they are internalized and used later. As a step towards solving this large and difficult problem, I have focused on how people learn to trust each other when they have a conversation. The model I have created represents people as sets of self-organizing maps; each has a map to represent his own beliefs, and a map to represent what he thinks of another person. Beliefs are in this model restricted to likes and dislikes, across a wide range of topics. In this thesis I describe the program implemented in Java to test this model. The model has been tested on four different kinds of conversations. with the topics of animals and cars, to determine whether its behavior looks reasonable to a human observer. In this work I show how a simple, natural model can closely approximate human behavior. without need for tweaking parameters. by Catherine Miller. M.Eng. 43 leaves eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Modeling trust in human conversation Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="053cb940-a47d-49e8-bc37-20dae58326d6"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Modeling trust in human conversation</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2006</PublicationDate> <Authors> <Author> <DisplayName>Miller, Catherine (Catherine T.)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>If we are ever to have intelligent systems, they will need memory. Memory is the core of learning; intelligence is about entering, extracting, and synthesizing its contents. What makes the memory problem difficult is that memory is not a simple collection of facts. The how and why of where those facts were acquired is a key part of how they are internalized and used later. As a step towards solving this large and difficult problem, I have focused on how people learn to trust each other when they have a conversation. The model I have created represents people as sets of self-organizing maps; each has a map to represent his own beliefs, and a map to represent what he thinks of another person. Beliefs are in this model restricted to likes and dislikes, across a wide range of topics. In this thesis I describe the program implemented in Java to test this model. The model has been tested on four different kinds of conversations. with the topics of animals and cars, to determine whether its behavior looks reasonable to a human observer. In this work I show how a simple, natural model can closely approximate human behavior. without need for tweaking parameters.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>