Analytical goal-driven learning of procedural knowledge by observation

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Abstract/Contents

Abstract
Knowledge-based approaches to planning and control offer benefits over classical techniques in applications that involve large yet structured state spaces. However, knowledge bases are time consuming and costly to construct. In this dissertation I introduce a framework for analytical learning that enables the agent to acquire generalizable, domain-specific procedural knowledge in the form of goal-indexed hierarchical task networks by observing a small number of successful demonstrations of goal-driven tasks. I discuss how, in contrast with most algorithms for learning by observation, my approach can learn from unannotated input demonstrations by automatically inferring the purpose of each solution step using the background knowledge about the domain. I discuss the role of hierarchical structure, distributed applicability conditions, and goals in the generalizability of the acquired knowledge. I also introduce an approach for adaptively determining the structure of the acquired knowledge that strikes a balance between generality and operationality, and for making the algorithm robust to changes in the structure of background knowledge. This involves resolving interdependencies among goals using temporal information. I present experimental studies on a number of domains which demonstrate that the quality of acquired knowledge is comparable to handcrafted content in terms of both coverage and complexity. In closing, I review related work and directions for future research.

Description

Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2011
Issuance monographic
Language English

Creators/Contributors

Associated with Nejati, Negin
Associated with Stanford University, Department of Electrical Engineering
Primary advisor Das, Amar K. (Amar Kumar)
Primary advisor Widrow, Bernard, 1929-
Thesis advisor Das, Amar K. (Amar Kumar)
Thesis advisor Widrow, Bernard, 1929-
Thesis advisor Langley, Pat
Advisor Langley, Pat

Subjects

Genre Theses

Bibliographic information

Statement of responsibility Negin Nejati.
Note Submitted to the Department of Electrical Engineering.
Thesis Thesis (Ph.D.)--Stanford University, 2011.
Location electronic resource

Access conditions

Copyright
© 2011 by Negin Nejati

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