提出量化多智能体系统责任的新方法，可衡量各智能体对结果的因果责任。
Measuring Responsibility in Multi-Agent Systems
- 基于概率交替时态逻辑，用三种指标关联行为与责任
- 引入熵基度量，首次实现对随时间变化的责任动态评估
- 适合研究多智能体协作或问责机制的研究者
我们提出了一类用于多智能体规划中的责任定量度量方法，基于Parker等人提出的因果责任概念。该方法在一种概率交替时态逻辑的变体中形式化，通过三个指标将行为与责任之间的概率关系联系起来，包括一种基于熵的责任度量。该度量是首个能捕捉随时间演进的结果因果责任的指标，提供反映达成结果难度的渐近测量。本方法为多智能体系统中的责任提供了新的理解，揭示了智能体在实现或防止特定结果中所起的定性与定量作用。
原文摘要 · Abstract (English)
We introduce a family of quantitative measures of responsibility in multi-agent planning, building upon the concepts of causal responsibility proposed by Parker et al.~[ParkerGL23]. These concepts are formalised within a variant of probabilistic alternating-time temporal logic. Unlike existing approaches, our framework ascribes responsibility to agents for a given outcome by linking probabilities between behaviours and responsibility through three metrics, including an entropy-based measurement of responsibility. This latter measure is the first to capture the causal responsibility properties of outcomes over time, offering an asymptotic measurement that reflects the difficulty of achieving these outcomes. Our approach provides a fresh understanding of responsibility in multi-agent systems, illuminating both the qualitative and quantitative aspects of agents' roles in achieving or preventing outcomes.
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