让多智能体在概率系统中兼顾责任与收益平衡地协作
Responsibility-aware Strategic Reasoning in Probabilistic Multi-Agent Systems
- 用带责任模态的逻辑框架实现责任感知的策略推理
- 通过纳什均衡优化责任分配与奖励获取的平衡
- 适合研究可信自主系统或责任分配机制的读者
责任在可信赖自主系统的发展与部署中起关键作用。本文聚焦于具有责任感知能力的多智能体系统的战略推理问题，提出一种名为PATL+R的概率交替时间逻辑变体。其创新之处在于引入因果责任模态，构建了责任感知的多智能体战略推理框架。我们提出一种联合策略合成方法，可满足在PATL+R中指定的结果，同时优化预期因果责任份额与奖励。该方法实现了责任与奖励增益在智能体间的均衡分配。为此，我们以纳什均衡作为战略推理问题的解概念，并通过将问题转化为并发随机多人博弈的参数化模型检验，实现责任感知纳什均衡策略的计算。
原文摘要 · Abstract (English)
Responsibility plays a key role in the development and deployment of trustworthy autonomous systems. In this paper, we focus on the problem of strategic reasoning in probabilistic multi-agent systems with responsibility-aware agents. We introduce the logic PATL+R, a variant of Probabilistic Alternating-time Temporal Logic. The novelty of PATL+R lies in its incorporation of modalities for causal responsibility, providing a framework for responsibility-aware multi-agent strategic reasoning. We present an approach to synthesise joint strategies that satisfy an outcome specified in PATL+R, while optimising the share of expected causal responsibility and reward. This provides a notion of balanced distribution of responsibility and reward gain among agents. To this end, we utilise the Nash equilibrium as the solution concept for our strategic reasoning problem and demonstrate how to compute responsibility-aware Nash equilibrium strategies via a reduction to parametric model checking of concurrent stochastic multi-player games.
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