用因果模型推断智能体的意图，让机器理解'为什么这么做'
Teleological Inference in Structural Causal Models via Intentional Interventions
- 引入无时间依赖的意图干预算子，构建结构化终态模型
- 通过反事实分析识别智能体行为并还原其目标意图
- 适用于自动驾驶、人机交互等需理解意图的场景
结构化因果模型（SCMs）最初用于回答因果问题。本文表明，SCMs也可用于表述和解答关于状态感知、目标导向智能体在因果系统中干预行为的意向性问题。我们回顾了以往建模此类智能体方法的局限性，提出一种新的时间无关干预算子——意图干预，并由此构建一个称为结构终态模型（SFM）的孪生因果模型。SFM将观测值视为意图干预的结果，将其与该干预的反事实条件（即若智能体未干预会如何）相联系。我们展示了SFM如何用于实证检测智能体及其意图发现。
原文摘要 · Abstract (English)
Structural causal models (SCMs) were conceived to formulate and answer causal questions. This paper shows that SCMs can also be used to formulate and answer teleological questions, concerning the intentions of a state-aware, goal-directed agent intervening in a causal system. We review limitations of previous approaches to modeling such agents, and then introduce intentional interventions, a new time-agnostic operator that induces a twin SCM we call a structural final model (SFM). SFMs treat observed values as the outcome of intentional interventions and relate them to the counterfactual conditions of those interventions (what would have happened had the agent not intervened). We show how SFMs can be used to empirically detect agents and to discover their intentions.
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