2026-09-21T14:29:27Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/270842022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Patrick Henry Winston.
Bevilacqua, John J. (John Joseph), 1980-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-06T21:39:41Z
2005-09-06T21:39:41Z
2004
2004
http://hdl.handle.net/1721.1/27084
56820806
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (leaf 56).
Computer decision making systems are aids that are becoming used in an increasing number and variety of applications. These systems allow people to interface with sources of information far too large and complicated to process themselves. As these systems become used on more advanced and complicated tasks, they will need to become more intelligent. One step towards this is the creation of a multi-agent decision making system that uses behavior inspired by the interactions of groups of people to allow a set of agents with humanistic characteristics to interact with one another. I implemented a program, AgentCommittee, which incorporates such behavior. AgentCommittee uses a set of characteristics that include extroversion, fatigue, resistance, confidence, and competitiveness in a series of one-on-one interactions to arrive at a group decision. The goal of AgentCommittee is not to find the best or optimal answer, but to produce a variety of good answers. I tested this program on a set of data from Compaq's EachMovie database and found that AgentCommittee was 20% more successful at finding relationships between the genre of a movie and a user's opinion of that movie than a random output generator.
by John J. Bevilacqua.
M.Eng.
56 leaves
2639867 bytes
2644644 bytes
application/pdf
application/pdf
en_US
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.
Decision making by agent committee
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="583021a1-65ba-4fed-899a-6466f3390814">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>en_US</Language>
<Title>Decision making by agent committee</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2004</PublicationDate>
<Authors>
<Author>
<DisplayName>Bevilacqua, John J. (John Joseph), 1980-</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>Computer decision making systems are aids that are becoming used in an increasing number and variety of applications. These systems allow people to interface with sources of information far too large and complicated to process themselves. As these systems become used on more advanced and complicated tasks, they will need to become more intelligent. One step towards this is the creation of a multi-agent decision making system that uses behavior inspired by the interactions of groups of people to allow a set of agents with humanistic characteristics to interact with one another. I implemented a program, AgentCommittee, which incorporates such behavior. AgentCommittee uses a set of characteristics that include extroversion, fatigue, resistance, confidence, and competitiveness in a series of one-on-one interactions to arrive at a group decision. The goal of AgentCommittee is not to find the best or optimal answer, but to produce a variety of good answers. I tested this program on a set of data from Compaq's EachMovie database and found that AgentCommittee was 20% more successful at finding relationships between the genre of a movie and a user's opinion of that movie than a random output generator.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>