Learning commonsense knowledge from the interpretation of individual experiences
Name
505529748-MIT.pdf
Description
Full printable version
Size
19.33 MB
Format
Adobe PDF
Checksum (MD5)
f7021e30bfcb4278f64eb9e5af01afb6
Author(s)
Glidden, Sam Wyatt
Advisor(s)
Patrick Winston.
Date Issued
2009
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Includes bibliographical references (p. 50).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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