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.

本研究针对概率多智能体系统中缺乏责任意识的战略推理问题，提出了一种新的逻辑PATL+R，结合了因果责任的模式，为责任意识的多智能体战略推理提供了框架。研究发现，该框架可以优化责任和奖励的期望分配，并通过纳什均衡计算出责任意识纳什均衡策略，显著提升了多智能体系统的可信性与表现。

责任意识的战略推理在概率多智能体系统中的应用