Proceedings of the Annual Meeting of the Cognitive Science Society

Modeling human sequential decision-making behavior presents a significant challenge for researchers in artificial intelligence, robotics, and cognitive science. In this paper, we introduce a human behavior model designed to predict actions in the Tower of London task, addressing two critical aspects that have been largely overlooked in existing methodologies. First, we propose a profile-based action prediction framework that extracts user and task profiles from historical data, enhancing action prediction in novel scenarios. Second, we introduce a replanning detection component that leverages thinking time as an indicator of planning processes in the human mind, enabling a more precise representation of cognitive dynamics. Our evaluations demonstrate the effectiveness of the proposed model, achieving superior performance in behavior prediction within the Tower of London task. This work lays the foundation for more robust human behavior modeling in sequential decision-making environments.