用反事实推理量化多智能体系统中各主体的责任归属。
Counterfactual Reasoning for Causal Responsibility Attribution in Probabilistic Multi-Agent Systems
- 基于反事实推理与博弈论建模，定义事后责任度量。
- 采用谢林值分配责任，确保公平性与一致性。
- 支持责任感知的系统验证与纳什均衡策略计算。
责任分配——确定智能体对结果承担程度——是多智能体系统设计与分析的核心挑战。本文将此类系统建模为并发随机多人博弈，提出一种事后（向后）反事实责任概念，用于量化给定策略组合下各智能体对结果的责任。通过引入谢林值进行责任分配，形式化证明该方法满足公平性与一致性等关键性质。在此基础上，构建了一个支持责任感知系统验证与战略推理的正式框架。进一步地，以纳什均衡为解概念，展示了如何计算在责任与期望收益之间权衡的稳定策略组合。
原文摘要 · Abstract (English)
Responsibility allocation -- determining the extent to which agents are accountable for outcomes -- is a fundamental challenge in the design and analysis of multi-agent systems. In this work, we model such systems as concurrent stochastic multi-player games and introduce a notion of retrospective (backward) counterfactual responsibility, which quantifies an agent's accountability for outcomes resulting from a given strategy profile. To allocate responsibility among agents, we utilise the Shapley value and formally show that this method satisfies key desirable properties, including fairness and consistency. Building on this foundation, we propose a formal framework that supports both verification and strategic reasoning in responsibility-aware multi-agent systems. Furthermore, by adopting Nash equilibrium as the solution concept, we demonstrate how to compute stable strategy profiles in which agents trade off responsibility against expected reward.
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