Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
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This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents and show that agents who have social inhibition can converge to a Nash stable partition (i.e., social agreement) using our proposed decentralised algorithm within polynomial time. The algorithm is simple and executable based on local interactions with neighbour agents under a strongly-connected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of a converged solution's suboptimality and additionally show that 50 social utilities are non-decreasing functions with respect to the number of co-working agents. Through numerical experiments, it is confirmed that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation where some of the agents temporarily somehow do not operate during a mission.
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