Imitating Human Responses via a Dual-Process Model Approach

Human-autonomous system teaming is becoming more prevalent in the Air Force and in society. Often, the concept of a shared mental model is discussed as a means to enhance collaborative work arrangements between a human and an autonomous system. The idea being that when the models are aligned, the team is more productive due to an increase in trust, predictability, and apparent understanding. This research presents the Dual-Process Model using multivariate normal probability density functions (DPM-MN), which is a cognitive architecture algorithm based on the psychological dual-process theory. The dual-process theory proposes a bipartite decision-making process in people. It labels the intuitive mode as “System 1” and the reflective mode as “System 2”. The current research suggests by leveraging an agent which forms decisions based on a dual-process model, an agent in a human-machine team can maintain a better shared mental model with the user. Evaluation of DPM-MN in a game called Space Navigator shows that DPM-MN presents a successful dual-process theory motivated model

A generic cognitive architecture framework with personality and emotions for crowd simulation

Crowd simulation has gained a great deal of attention recently due to its essential use in games and diverse 3D simulation. Incorporating psychological aspects into agents is an important task in order to build human-like agents in crowd simulation. However, traditional approaches incorporate psychological aspects into low-level agent parameters thus only work in specific environments. This paper proposes a generic cognitive architecture framework that implements psychological aspects to create human-like agents for general-purpose crowd simulation. We also present a computational model as a proof-of-concept