The ability to anticipate team members' actions enables joint action towards a common goal. Task knowledge and mental simulation allow for anticipating other agents' actions and for making inferences about their underlying mental representations. In human–AI teams, providing AI agents with anticipatory mechanisms can facilitate collaboration and successful execution of joint action. This paper presents a computational cognitive model demonstrating mental simulation of operators' mental models of a situation and anticipation of their behavior. The work proposes two successive steps: (1) A hierarchical cluster algorithm is applied to recognize patterns of behavior among pilots. These behavioral clusters are used to derive commonalities in situation models from empirical data (N = 13 pilots). (2) An ACT-R (adaptive control of thought - rational) cognitive model is implemented to mentally simulate different possible outcomes of action decisions and timing of a pilot. model tracing of ACT-R allows following up on operators' individual actions. Two models are implemented using the symbolic representations of ACT-R: one simulating normative behavior and the other by simulating individual differences and using subsymbolic learning. Model performance is analyzed by a comparison of both models. Results indicate the improved performance of the individual differences over the normative model and are discussed regarding implications for cognitive assistance capable of anticipating operator behavior.

中文翻译：

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预期的认知建模：飞行员心理表征的无监督学习和符号建模

预测团队成员行动的能力可以实现共同目标的联合行动。任务知识和心理模拟允许预测其他代理的行为并推断他们潜在的心理表征。在人类-人工智能团队中，为人工智能代理提供预期机制可以促进协作和联合行动的成功执行。本文提出了一个计算认知模型，展示了操作员对情境的心理模型和对其行为的预期的心理模拟。这项工作提出了两个连续的步骤：（1）应用层次聚类算法来识别飞行员之间的行为模式。这些行为集群用于从经验数据（N =13 名飞行员）。(2) 采用ACT-R（自适应思维控制-理性）认知模型，在心理上模拟飞行员的动作决策和时机的不同可能结果。ACT-R 的模型跟踪允许跟踪操作员的个人操作。使用 ACT-R 的符号表示实现了两个模型：一个模拟规范行为，另一个通过模拟个体差异和使用亚符号学习。通过比较两种模型来分析模型性能。结果表明个体差异的表现优于规范模型，并讨论了对能够预测操作员行为的认知辅助的影响。