通过博弈诱导的非线性意见动态实现涌现协调
Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics
AI总结：
提出一种基于动态博弈价值函数合成非线性意见动态（NOD）的多智能体决策框架，解决了意图未定时的协调死锁问题，并提供了均衡稳定性条件和基于优化的轨迹优化算法。
摘要：
我们提出了一种多智能体决策框架，用于实现意图最初未定的自主智能体的涌现协调。动态非合作博弈已被用于编码多智能体交互，但由目标偏好或多重均衡存在等因素引起的模糊性可能导致协调问题，从“机器人冻结”问题到安全关键事件中的不安全行为。最近开发的非线性意见动态（NOD）为打破死锁提供了保证。然而，在一般多智能体环境中自动选择合适的模型参数仍然是一个挑战。在本文中，我们首先提出了一种新颖且有原则的程序，用于基于以智能体意图为条件的动态博弈的价值函数来合成 NOD。特别地，我们为双人两选项情况提供了基于智能体意见与其博弈价值之间不匹配的博弈诱导 NOD 均衡的精确稳定性条件。然后，我们提出了一种基于优化的轨迹优化算法，该算法计算由意见演化引导的智能体策略。我们的方法的有效性通过一个模拟收费站协调示例来说明。
英文摘要：
We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided. Dynamic non-cooperative games have been used to encode multi-agent interaction, but ambiguity arising from factors such as goal preference or the presence of multiple equilibria may lead to coordination issues, ranging from the "freezing robot" problem to unsafe behavior in safety-critical events. The recently developed nonlinear opinion dynamics (NOD) provide guarantees for breaking deadlocks. However, choosing the appropriate model parameters automatically in general multi-agent settings remains a challenge. In this paper, we first propose a novel and principled procedure for synthesizing NOD based on the value functions of dynamic games conditioned on agents' intents. In particular, we provide for the two-player two-option case precise stability conditions for equilibria of the game-induced NOD based on the mismatch between agents' opinions and their game values. We then propose an optimization-based trajectory optimization algorithm that computes agents' policies guided by the evolution of opinions. The efficacy of our method is illustrated with a simulated toll station coordination example.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
