Learning to infer final plans in human team planning
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 for...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
International Joint Conferences on Artificial Intelligence
2020
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| Online Access: | https://hdl.handle.net/1721.1/125887 |
| _version_ | 1826206866617663488 |
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| author | Kim, Joseph Woicik, Matthew E. Gombolay, Matthew C. Son, Sung-Hyun Shah, Julie A |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Kim, Joseph Woicik, Matthew E. Gombolay, Matthew C. Son, Sung-Hyun Shah, Julie A |
| author_sort | Kim, Joseph |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:39:29Z |
| format | Article |
| id | mit-1721.1/125887 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:39:29Z |
| publishDate | 2020 |
| publisher | International Joint Conferences on Artificial Intelligence |
| record_format | dspace |
| spelling | mit-1721.1/1258872022-09-28T15:21:36Z Learning to infer final plans in human team planning Kim, Joseph Woicik, Matthew E. Gombolay, Matthew C. Son, Sung-Hyun Shah, Julie A Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Lincoln Laboratory 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. 2020-06-19T16:58:12Z 2020-06-19T16:58:12Z 2018 2019-11-01T12:33:10Z Article http://purl.org/eprint/type/ConferencePaper 978-0-9992411-2-7 https://hdl.handle.net/1721.1/125887 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) en 10.24963/IJCAI.2018/663 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf International Joint Conferences on Artificial Intelligence MIT web domain |
| spellingShingle | Kim, Joseph Woicik, Matthew E. Gombolay, Matthew C. Son, Sung-Hyun Shah, Julie A Learning to infer final plans in human team planning |
| title | Learning to infer final plans in human team planning |
| title_full | Learning to infer final plans in human team planning |
| title_fullStr | Learning to infer final plans in human team planning |
| title_full_unstemmed | Learning to infer final plans in human team planning |
| title_short | Learning to infer final plans in human team planning |
| title_sort | learning to infer final plans in human team planning |
| url | https://hdl.handle.net/1721.1/125887 |
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