Sidekick agents for sequential planning problems
Name
868823103-MIT.pdf
Description
Full printable version
Size
9.5 MB
Format
Adobe PDF
Checksum (MD5)
8f04d65d3eeb6b0a693f8534e4cb2943
Author(s)
Macindoe, Owen
Advisor(s)
Leslie Pack Kaelbling and Tomás Lozano-Pérez.
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
Effective Al sidekicks must solve the interlinked problems of understanding what their human collaborator's intentions are and planning actions to support them. This thesis explores a range of approximate but tractable approaches to planning for AI sidekicks based on decision-theoretic methods that reason about how the sidekick's actions will effect their beliefs about unobservable states of the world, including their collaborator's intentions. In doing so we extend an existing body of work on decision-theoretic models of assistance to support information gathering and communication actions. We also apply Monte Carlo tree search methods for partially observable domains to the problem and introduce an ensemble-based parallelization strategy. These planning techniques are demonstrated across a range of video game domains.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 127-131).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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reproduction or distribution in any format is prohibited without written
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