Identification method for collective consensus mechanism based on inverse reinforcement learning

Abstract

<p indent="0mm">Collective intelligence, an important topic of the new generation of artificial intelligence, is a significant way to solve large-scale and complex problems in an open environment. It is crucial in other branches of artificial intelligence. Agents interact and evolve in response to the consensus mechanism to reach a group consensus. Identifying the consensus mechanism is essential for building and comprehending the collective intelligence system. Traditional consensus mechanism modeling methods require many simplified assumptions, and meeting the challenge of complex collective intelligence systems is difficult. A method for identifying consensus mechanisms based on data should be developed. In this paper, the consensus mechanism’s identification problem is transformed into an inverse reinforcement learning problem for the collective intelligence system. We proposed inverse reinforcement learning methods for collective systems and evaluated them in two tasks. The results indicate that the proposed methods can identify the policy function and the reward function of the collective system.