论文标题
在对抗环境中用于合作多代理决策的游戏理论实用网络
A Game-Theoretic Utility Network for Cooperative Multi-Agent Decisions in Adversarial Environments
论文作者
论文摘要
在危险场景中，多代理系统（MAS）之间的基本关系可以表示为游戏理论模型。我们从平衡成功概率和系统成本的角度来衡量MAS完成任务的性能。本文提出了一种新的基于网络的模型，称为游戏理论实用树（GUT），该模型将高级策略分解为可执行的低级动作，以进行合作MAS决策。这与基于实时策略游戏的代理需求的新回报措施相结合。我们提出了一个探索游戏领域，以评估肠道针对最先进的QMIX决策方法。大量数值模拟的结论结果表明，肠道可以组织MAS合作之间更复杂的关系，从而帮助小组以较低的成本和更高的获胜率来实现具有挑战性的任务。
Underlying relationships among multi-agent systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. We measure the performance of MAS achieving tasks from the perspective of balancing success probability and system costs. This paper proposes a new network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. This is combined with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain to evaluate GUT against the state-of-the-art QMIX decision-making method. Conclusive results on extensive numerical simulations indicate that GUT can organize more complex relationships among MAS cooperation, helping the group achieve challenging tasks with lower costs and a higher winning rate.
