Responsibility attribution is a key concept of accountable multi-agent decision making. Given a sequence of actions, responsibility attribution mechanisms quantify the impact of each participating agent to the final outcome. One such popular mechanism is based on actual causality, and it assigns (causal) responsibility based on the actions that were found to be pivotal for the considered outcome. However, the inherent problem of pinpointing actual causes and consequently determining the exact responsibility assignment has shown to be computationally intractable. In this paper, we aim to provide a practical algorithmic solution to the problem of responsibility attribution under a computational budget. We first formalize the problem in the framework of Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) augmented by a specific class of Structural Causal Models (SCMs). Under this framework, we introduce a Monte Carlo Tree Search (MCTS) type of method which efficiently approximates the agents' degrees of responsibility. This method utilizes the structure of a novel search tree and a pruning technique, both tailored to the problem of responsibility attribution. Other novel components of our method are (a) a child selection policy based on linear scalarization and (b) a backpropagation procedure that accounts for a minimality condition that is typically used to define actual causality. We experimentally evaluate the efficacy of our algorithm through a simulation-based test-bed, which includes three team-based card games.

翻译：责任归因是可问责多智能体决策中的关键概念。给定一系列行动，责任归因机制量化每个参与智能体对最终结果的影响。其中一种流行的机制基于实际因果关系，它根据被认定为对结果起关键作用的行动来分配（因果）责任。然而，精确定位实际原因并由此确定确切责任分配这一固有难题已被证明在计算上难以处理。本文旨在提供一种在计算预算限制下解决责任归因问题的实用算法。我们首先在配备了特定结构因果模型（SCMs）类的分散式部分可观测马尔可夫决策过程（Dec-POMDPs）框架下形式化该问题。在此框架下，我们引入一种蒙特卡洛树搜索（MCTS）类方法，该方法能够高效近似各智能体的责任程度。该方法利用针对责任归因问题定制的全新搜索树结构及剪枝技术。我们方法的其他新颖组成部分包括：(a) 基于线性标量化的子节点选择策略，以及(b) 考虑通常用于定义实际因果关系的最小性条件的反向传播过程。我们通过基于仿真的测试平台（包含三种团队卡牌游戏）实验评估了我们算法的有效性。