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Yinon Douchan, Ran Wolf, and Gal A. Kaminka. Swarms Can be Rational.
In Proceedings of the International Joint Conference on Autonomous Agents and Multi-Agent Systems, 2019.
Multi-robot systems are comprised of multiple robots, each under its own control, typically carrying out tasks towards a global goal. In these, spatial coordination (avoiding collisions) is a fundamental challenge. Swarm methods, where by robots coordinate ad-hoc and locally, with little or no communications offer a promising approach. However, while empirically demonstrated to be viable in practice, no theoretical guarantees of performance are known, nor a formalization of the task in a way that admits analysis. This paper formalizes a class of multi-robot cooperative tasks as potential extensive-form games. We show that the system coordination overhead is a potential function, forming a connection between the theoretical maximum-payoff equilibrium of the system, and the rational choices of individual robots during task execution: the robot swarm can be rational in theory. We then show how to approximate the rational decision-making in practice using reinforcement learning, while operating strictly within the limited capabilities of simple swarm robots. We empirically evaluate the efficacy of these methods in two multi-robot domains.
@inproceedings{aamas19,
author = {Yinon Douchan and Ran Wolf and Gal A. Kaminka},
title = {Swarms Can be Rational},
booktitle = AAMAS,
year = {2019},
wwwnote = {},
abstract = {
Multi-robot systems are comprised of multiple robots, each under its own control, typically carrying out tasks towards a global goal. In these, spatial coordination (avoiding collisions) is a fundamental challenge. Swarm methods, where by robots coordinate ad-hoc and locally, with little or no communications offer a promising approach. However, while empirically demonstrated to be viable in practice, no theoretical guarantees of performance are known, nor a formalization of the task in a way that admits analysis.
This paper formalizes a class of multi-robot cooperative tasks as potential extensive-form games.
We show that the system coordination overhead is a potential function, forming a connection between the theoretical maximum-payoff equilibrium of the system, and the rational choices of individual robots during task execution: the robot swarm can be rational in theory. We then show how to approximate the rational decision-making in practice using reinforcement learning, while operating strictly within the limited capabilities of simple swarm robots. We empirically evaluate the efficacy of these methods in two multi-robot domains.
}
}
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