Human-agent coordination in a group formation game

Abstract

Coordination and cooperation between humans and autonomous agents in cooperative games raise interesting questions on human decision making and behaviour changes. Here we report our findings from a group formation game in a small-world network of different mixes of human and agent players, aiming to achieve connected clusters of the same colour by swapping places with neighbouring players using non-overlapping information. In the experiments the human players are incentivized by rewarding to prioritize their own cluster while the model of agents’ decision making is derived from our previous experiment of purely cooperative game between human players. The experiments were performed by grouping the players in three different setups to investigate the overall effect of having cooperative autonomous agents within teams. We observe that the human subjects adjust to autonomous agents by being less risk averse, while keeping the overall performance efficient by splitting the behaviour into selfish and cooperative actions performed during the rounds of the game. Moreover, results from two hybrid human-agent setups suggest that the group composition affects the evolution of clusters. Our findings indicate that in purely or lesser cooperative settings, providing more control to humans could help in maximizing the overall performance of hybrid systems.

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“…As artificial intelligence (AI) and machine learning continue to evolve, the interdependent interactions between intelligent agents (IAs), such as Chat-GPT, Siri, Alexa, and Google Assistant, are becoming increasingly integral to daily life [1]. This growing prevalence has led to the emergence of Human-Agent Teams (HATs), where IAs perform a variety of roles, such as task automation, augmentation, and decision-making [2,3].…”

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