Multi-Robot Task Allocation Using Global Games with Negative Feedback: The Colony Maintenance Problem

In this article we address the multi-robot task allocation problem, where robots must cooperatively assign themselves to accomplish a set of tasks in their environment. We consider the colony maintenance problem as an example, where a team of robots are tasked with continuously maintaining the energy supply of a central colony. We model this as a global game, where each robot measures the energy level of the colony, and the current number of assigned robots, to determine whether or not to forage for energy sources. The key to our approach is introducing a negative feedback term into the robots' utility, which also eliminates the trivial solution where foraging or not foraging are strictly dominant strategies. We compare our approach qualitatively to existing global games, where a positive positive feedback term admits threshold-based decision making, and encourages many robots to forage simultaneously. We show how positive feedback can lead to a cascading failure in the presence of a human who recruits robots, and we demonstrate the resilience of our approach in simulation.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i13.33485
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
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article

Multi-Robot Task Allocation Using Global Games with Negative Feedback: The Colony Maintenance Problem

Logan E. Beaver
Proceedings of the AAAI Conference on Artificial Intelligence
Scheduling and Optimization Algorithms
article

Multi-Robot Task Allocation Using Global Games with Negative Feedback: The Colony Maintenance Problem

Logan E. Beaver
article en

Abstract

In this article we address the multi-robot task allocation problem, where robots must cooperatively assign themselves to accomplish a set of tasks in their environment. We consider the colony maintenance problem as an example, where a team of robots are tasked with continuously maintaining the energy supply of a central colony. We model this as a global game, where each robot measures the energy level of the colony, and the current number of assigned robots, to determine whether or not to forage for energy sources. The key to our approach is introducing a negative feedback term into the robots' utility, which also eliminates the trivial solution where foraging or not foraging are strictly dominant strategies. We compare our approach qualitatively to existing global games, where a positive positive feedback term admits threshold-based decision making, and encourages many robots to forage simultaneously. We show how positive feedback can lead to a cascading failure in the presence of a human who recruits robots, and we demonstrate the resilience of our approach in simulation.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(13)
Dominion University College (GH), Old Dominion University (US)
Openalex Percentile: Top 81%
Scheduling and Optimization Algorithms
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Multi-Robot Task Allocation Using Global Games with Negative Feedback: The Colony Maintenance Problem — Logan E. Beaver · Proceedings of the AAAI Conference on Artificial Intelligence (2025) | TGRS Research Map | TGRS