Learning to infer final plans in human team planning
Name
kim-ijcai18-preprint.pdf
Description
Accepted version
Size
453.4 KB
Format
Adobe PDF
Checksum (MD5)
196b0c62618388315b060cb8344e5e02
Author(s)
Kim, Joseph
Woicik, Matthew E.
Gombolay, Matthew C.
Son, Sung-Hyun
Shah, Julie A
Date Issued
2018
Journal
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Publisher
International Joint Conferences on Artificial Intelligence
Citation
Kim, Joseph, et al., "Learning to infer final plans in human team planning." Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, July 13-19, 2018, edited by Jérôme Lang, International Joint Conferences on Artificial Intelligence, 2018: p. 4771-79 doi 10.24963/IJCAI.2018/663 ©2018 Author(s)
Version
Author's final manuscript
Abstract
We envision an intelligent agent that analyzes conversations during human team meetings in order to infer the team's plan, with the purpose of providing decision support to strengthen that plan. We present a novel learning technique to infer teams' final plans directly from a processed form of their planning conversation. Our method employs reinforcement learning to train a model that maps features of the discussed plan and patterns of dialogue exchange among participants to a final, agreed-upon plan. We employ planning domain models to efficiently search the large space of possible plans, and the costs of candidate plans serve as the reinforcement signal. We demonstrate that our technique successfully infers plans within a variety of challenging domains, with higher accuracy than prior art. With our domain-independent feature set, we empirically demonstrate that our model trained on one planning domain can be applied to successfully infer team plans within a novel planning domain.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Lincoln Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.24963/IJCAI.2018/663
