Inferring final plans : expanding on a generative and logic-based approach

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.

Bibliographic Details
Main Author: Johnson, Brittney E
Other Authors: Julie A. Shah.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2018
Subjects:
Online Access:http://hdl.handle.net/1721.1/113141
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author Johnson, Brittney E
author2 Julie A. Shah.
author_facet Julie A. Shah.
Johnson, Brittney E
author_sort Johnson, Brittney E
collection MIT
description Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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spelling mit-1721.1/1131412019-04-11T00:56:13Z Inferring final plans : expanding on a generative and logic-based approach Expanding on a generative and logic-based approach Johnson, Brittney E Julie A. Shah. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 91-93). When humans work together to form a plan, they often make mistakes: they misspeak, say things out of order, and negate things they had previously said. We aim to read human team planning conversations and extract the final agreed-upon plan so that a robotic agent may assist in design or execution. Previous work shows that a generative model with logic-based priors is effective when the plan being formed is relatively simple. We present an algorithm that expands on the model by incorporating dialogue acts, which give an indication of how proposed actions are said. We compare our model's performance to humans on the same task. We also validate the model on a toy problem, achieving the desired output 8 times out of 10 (compared to a baseline of 3/10), and run the baseline and our expanded model on a more complex input dialogue. To the best of our knowledge, this is this first work that incorporates dialogue acts into a generative model to perform plan inference. by Brittney E. Johnson. M. Eng. 2018-01-12T20:59:13Z 2018-01-12T20:59:13Z 2017 2017 Thesis http://hdl.handle.net/1721.1/113141 1017990146 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 93 pages application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Johnson, Brittney E
Inferring final plans : expanding on a generative and logic-based approach
title Inferring final plans : expanding on a generative and logic-based approach
title_full Inferring final plans : expanding on a generative and logic-based approach
title_fullStr Inferring final plans : expanding on a generative and logic-based approach
title_full_unstemmed Inferring final plans : expanding on a generative and logic-based approach
title_short Inferring final plans : expanding on a generative and logic-based approach
title_sort inferring final plans expanding on a generative and logic based approach
topic Electrical Engineering and Computer Science.
url http://hdl.handle.net/1721.1/113141
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