Resolving the Resource Decision-Making Dilemma of Leaderless Group-Based Multiagent Systems and Repeated Games
提出决策奖励区分框架，通过建模无领导群体的资源博弈过程，使智能体采用报复策略避免竞争困境并实现群体奖励最优。
Leaderless rational individuals often lead the group into a resource decision dilemma in resource competition. Reducing the cost of resource competition while avoiding group decision dilemmas is a challenging task. Inspired by multiagent systems (MASs) and repeated games, we propose a decision-making reward discrimination (DRD) framework to address the resource competition dilemma of leaderless group formation. We aim to model the leaderless group’s resource gaming process using MAS and achieve optimal rewards for the group while minimizing conflict in resource competition. The proposed framework consists of three modules: 1) the decision-making module; 2) the reward module; and 3) the discriminative module. The decision-making module defines the agents and models the decision-making process, while the reward module calculates the group reward in each round using the reward matrix. The discriminative module compares the group reward with the target reward while providing the agent with environmental information. We verify the feasibility of the model through numerous experiments. The results show that agents adopt a revenge strategy to avoid resource competition dilemmas and achieve group reward optimality.