Learning commonsense knowledge from the interpretation of individual experiences
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2010
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| Online Access: | http://hdl.handle.net/1721.1/53146 |
| _version_ | 1826200653522796544 |
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| author | Glidden, Sam Wyatt |
| author2 | Patrick Winston. |
| author_facet | Patrick Winston. Glidden, Sam Wyatt |
| author_sort | Glidden, Sam Wyatt |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. |
| first_indexed | 2024-09-23T11:39:46Z |
| format | Thesis |
| id | mit-1721.1/53146 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:39:46Z |
| publishDate | 2010 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/531462019-04-12T07:22:57Z Learning commonsense knowledge from the interpretation of individual experiences Glidden, Sam Wyatt Patrick Winston. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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. 2010-03-25T15:06:56Z 2010-03-25T15:06:56Z 2009 2009 Thesis http://hdl.handle.net/1721.1/53146 505529748 eng 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 50 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Glidden, Sam Wyatt Learning commonsense knowledge from the interpretation of individual experiences |
| title | Learning commonsense knowledge from the interpretation of individual experiences |
| title_full | Learning commonsense knowledge from the interpretation of individual experiences |
| title_fullStr | Learning commonsense knowledge from the interpretation of individual experiences |
| title_full_unstemmed | Learning commonsense knowledge from the interpretation of individual experiences |
| title_short | Learning commonsense knowledge from the interpretation of individual experiences |
| title_sort | learning commonsense knowledge from the interpretation of individual experiences |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/53146 |
| work_keys_str_mv | AT gliddensamwyatt learningcommonsenseknowledgefromtheinterpretationofindividualexperiences |