2026-09-19T00:35:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/769932022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Howard Reubenstein and Leslie Pack Kaelbling.
Nga, Hout (Hout L.)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-02-14T15:36:05Z
2013-02-14T15:36:05Z
2011
2011
http://hdl.handle.net/1721.1/76993
825553975
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 34-35).
The DARPA Bootstrapped Learning project uses relational learners to ladder concepts together to teach a final concept, essentially narrowing the search space at each step. However, there are many ways to structure background knowledge to teach a concept and it is uncertain how different ways of structuring knowledge affects the accuracy and performance of learning. In this paper, we examine the effect of having intermediate concepts when learning high level concepts. We used Quinlan's First Order Inductive Learner to learn target selection for a real-time strategy game and did cross-validation tests with varying levels of intermediate concept support. The results show that including intermediate concepts does not always improve performance and accuracy.
by Hout (Peter) Nga.
M.Eng.
42 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
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
The effect of intermediate concepts in hierarchical relational learning
Thesis
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Publication
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<Abstract>The DARPA Bootstrapped Learning project uses relational learners to ladder concepts together to teach a final concept, essentially narrowing the search space at each step. However, there are many ways to structure background knowledge to teach a concept and it is uncertain how different ways of structuring knowledge affects the accuracy and performance of learning. In this paper, we examine the effect of having intermediate concepts when learning high level concepts. We used Quinlan's First Order Inductive Learner to learn target selection for a real-time strategy game and did cross-validation tests with varying levels of intermediate concept support. The results show that including intermediate concepts does not always improve performance and accuracy.</Abstract>
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