COMMON PROBLEMS AND HELPFUL HINTS TO SOLVE THEM: LESSONS LEARNED IN INTEGRATING COGNITIVE MODELS IN LARGE-SCALE SIMULATION ENVIRONMENTS

摘要

The application of M&S simulation technologies to advanced analysis and training functions throughout the DoD has led to an increasing need for higher fidelity representations of human decision-making behavior than is currently available in most military simulation behavior engines. The appropriate path to meet this need is to incorporate cognitive models from the Human Behavior Representation (HBR) community that provide psychologically-rooted representations of decision-making behavior and performance. There are significant challenges associated with the integration of these models within complex simulation environments, However. Here, we attempt to identify some of these challenges and provide design strategies to overcome them. Specifically, we provide strategies for selecting appropriate modeling resolution for specific applications, dynamically managing the resolution of those models throughout a simulation run, and dealing with the general mismatch of sensor and control data between simulation environments and HBR models.