Abstract
Multi-robot teams are useful in a variety of task allocation domains such as warehouse automation and surveillance. Robots in such domains perform tasks at given locations and specific times, and are allocated tasks to optimize given team objectives. We propose an efficient, satisficing and centralized Monte Carlo Tree Search based algorithm exploiting branch and bound paradigm to solve the multi-robot task allocation problem with spatial, temporal and other side constraints. Unlike previous heuristics proposed for this problem, our approach offers theoretical guarantees and finds optimal solutions for some non-Trivial data sets.
| Original language | English (US) |
|---|---|
| Title of host publication | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
| Publisher | AAAI press |
| Pages | 4222-4223 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781577357605 |
| State | Published - 2016 |
| Event | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States Duration: Feb 12 2016 → Feb 17 2016 |
Publication series
| Name | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
|---|
Other
| Other | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
|---|---|
| Country/Territory | United States |
| City | Phoenix |
| Period | 2/12/16 → 2/17/16 |
Bibliographical note
Publisher Copyright:© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.