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

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Johnson, Brittney E

Advisor dc:contributor.advisor

• Julie A. Shah.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/113141

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/113141