We present a novel decentralized approach to allocate agents to tasks whose costs increase over time. Our model accounts for both the natural growth of the tasks and the effort of the agents at containing such growth. The objective is to minimize the increase in task costs. We show how a distributed coordination algorithm, which is based on max-sum, can be formulated to include costs of tasks that grow over time. Considering growing costs enables our approach to solve a wider range of problems than existing methods. We compare our approach against state-of-the-art methods in both a simple simulation and RoboCup Rescue simulation.
|Original language||English (US)|
|Title of host publication||Intelligent Autonomous Systems 14 - Proceedings of the 14th International Conference IAS-14|
|Editors||Weidong Chen, Hesheng Wang, Koh Hosoda, Emanuele Menegatti, Masahiro Shimizu|
|Number of pages||14|
|State||Published - 2017|
|Event||14th International Conference on Intelligent Autonomous Systems, IAS 2016 - Shanghai, China|
Duration: Jul 3 2016 → Jul 7 2016
|Name||Advances in Intelligent Systems and Computing|
|Other||14th International Conference on Intelligent Autonomous Systems, IAS 2016|
|Period||7/3/16 → 7/7/16|
Bibliographical noteFunding Information:
Work supported in part by NSF-IIP-1439728 and the Graduate School of the University of Minnesota.
© Springer International Publishing AG 2017.
- Binary max-sum
- Multi-robot systems
- Task allocation