2026-09-19T17:13:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/531462022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Patrick Winston.
Glidden, Sam Wyatt
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-03-25T15:06:56Z
2010-03-25T15:06:56Z
2009
2009
http://hdl.handle.net/1721.1/53146
505529748
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Includes bibliographical references (p. 50).
To understand human intelligence, we need to discover how we learn commonsense knowledge. We humans are able to infer generalizations of knowledge, make predictions about the future, and answer questions based on what we've experienced. In this thesis I present a method for performing these commonsense tasks in an artificial intelligence system. I introduce a data type called a chain which clusters together similar experiences. I use graphs to store readily available historical and causal relations for experiences. The resulting memory system can handle three types of commonsense reasoning tasks. It can generalize, going from two specific examples to the knowledge that all birds can fly. It can predict, hypothesizing that since a dog likes to bark at people, it will bark when a burglar appears. And it can answer questions, providing a response when asked about the location of my car. This memory system is encoded in approximately 2,000 lines of Java.
by Sam Wyatt Glidden.
M.Eng.
50 p.
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.
Learning commonsense knowledge from the interpretation of individual experiences
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="69adc210-15f8-4da2-9dd5-cf89466b3a6e">
<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>Learning commonsense knowledge from the interpretation of individual experiences</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2009</PublicationDate>
<Authors>
<Author>
<DisplayName>Glidden, Sam Wyatt</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>To understand human intelligence, we need to discover how we learn commonsense knowledge. We humans are able to infer generalizations of knowledge, make predictions about the future, and answer questions based on what we've experienced. In this thesis I present a method for performing these commonsense tasks in an artificial intelligence system. I introduce a data type called a chain which clusters together similar experiences. I use graphs to store readily available historical and causal relations for experiences. The resulting memory system can handle three types of commonsense reasoning tasks. It can generalize, going from two specific examples to the knowledge that all birds can fly. It can predict, hypothesizing that since a dog likes to bark at people, it will bark when a burglar appears. And it can answer questions, providing a response when asked about the location of my car. This memory system is encoded in approximately 2,000 lines of Java.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>