Hybrid Multi-Agent Strategy Discovering Algorithm for human behavior

Training in simulators through serious games is widely used in domains where it is too dangerous to train in a real environment. Simulations can help to model complex social and psychological aspects and can enable repetitiveness during game-based learning, which is especially important when opposing or cooperating humans can get hurt. When a trainee team interacts with other humans or software agents with human-like performance, cognitive and psychological properties and interactions that arise in various situations play an important role in serious game training. Therefore, special tools and methods that integrate physical and cognitive activities need to be developed in order to analyze the way trainees tackle the scenario. We have addressed these problems with the Hybrid Multi-Agent Strategy Discovering Algorithm (HMASDA), which builds upon an existing algorithm for physical strategy identification (MASDA) by adding the ability to process and consider cognitive models. To include the cognitive behavior of trainees, and to identify integrated policies based on their overall behavior, we introduced additional features that take into account the trainees' cognitive state, their well-being, and their emotional reactions. Using a predefined asymmetric conflict scenario, we demonstrate that it is possible to obtain physical and cognitive descriptions of the behavior that trainees display.