Inferring final plans : expanding on a generative and logic-based approach
Name
1017990146-MIT.pdf
Description
Full printable version
Size
1.89 MB
Format
Adobe PDF
Checksum (MD5)
5e2863f3aebb9b06a838c72937da223a
Author(s)
Johnson, Brittney E
Advisor(s)
Julie A. Shah.
Alternative Title
Expanding on a generative and logic-based approach
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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