Dataset used in the article: Evaluation of goal recognition systems on unreliable data and uninspectable agents

Goal or intent recognition, where one agent recognizes the goals or intentions of another, can be a powerful tool for effective teamwork and improving interaction between agents. Such reasoning can be challenging to perform, however, because observations of an agent can be unreliable and, often, an agent does not have access to the reasoning processes and mental models of the other agent. Despite this difficulty, recent work has made great strides in addressing these challenges. In particular, two Artificial Intelligence (AI)-based approaches to goal recognition have recently been shown to perform well: goal recognition as planning, which reduces a goal recognition problem to the problem of plan generation; and Combinatory Categorical Grammars (CCGs), which treat goal recognition as a parsing problem. Additionally, new advances in cognitive science with respect to Theory of Mind reasoning have yielded an approach to goal recognition that leverages analogy in its decision making. H...智能体（Agent）对另一智能体目标或意图的识别，可成为实现高效团队协作、优化智能体间交互的有力工具。 然而，此类推理任务往往颇具挑战：一方面，对智能体的观测可能存在不可靠性；另一方面，智能体通常无法获取另一智能体的推理过程与心理模型。尽管存在上述难点，近期相关研究已在应对这些挑战方面取得了显著进展。 具体而言，目前已有两类基于人工智能（Artificial Intelligence, AI）的目标识别方法展现出优异性能：其一为规划式目标识别方法，即将目标识别问题归约为规划生成问题；其二为组合范畴语法（Combinatory Categorical Grammars, CCGs），该方法将目标识别视作句法解析问题。此外，认知科学领域在心理理论（Theory of Mind）推理方面的新进展，也催生了一类在决策中借助类比推理的目标识别方法。 H...