将因果模型与多智能体博弈结合，分析策略决策的因果影响。
Causes and Strategies in Multiagent Systems
- 构建因果并发博弈结构，用干预模拟智能体行为
- 基于Halpern-Pearl框架计算变量间的因果效应
- 适合研究智能体策略因果关系的学者和工程师
因果关系在日常过程、人类推理和人工智能中起着关键作用，但在多智能体战略场景中的研究仍不充分。本文提出一种系统化方法，将给定的结构因果模型转化为并发博弈结构。在得到的因果并发博弈结构中，状态转移对应对原因果模型中智能体变量的干预。采用Halpern和Pearl的因果框架，判断某一变量取值对其他变量的影响。该结构使我们能够分析和推理智能体战略决策的因果效应。论文形式化研究了因果并发博弈结构与原始结构因果模型之间的关系。
原文摘要 · Abstract (English)
Causality plays an important role in daily processes, human reasoning, and artificial intelligence. There has however not been much research on causality in multi-agent strategic settings. In this work, we introduce a systematic way to build a multi-agent system model, represented as a concurrent game structure, for a given structural causal model. In the obtained so-called causal concurrent game structure, transitions correspond to interventions on agent variables of the given causal model. The Halpern and Pearl framework of causality is used to determine the effects of a certain value for an agent variable on other variables. The causal concurrent game structure allows us to analyse and reason about causal effects of agents' strategic decisions. We formally investigate the relation between causal concurrent game structures and the original structural causal models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。