Modeling Perceptual Judgement in Believable Agents: A Signal Detection Approach
Presented at the joint annual meetings of the Society for Mathematical Psychology and the International Conference on Cognitive Modelling, Madison, WI (July 2018).
A Warfighter in a combat environment is expected to continuously search his or her visual field to maintain situational awareness. Misidentification of relevant stimuli, such as failure to detect an enemy combatant or incorrect identification of a friend as an enemy, has costly results for the Warfighter and associated team members. By-products of situational demands, such as stress and fatigue, can significantly impact operational performance via moderation of perceptual processes. It is of paramount importance to understand perceptual judgment processes in individual Warfighters when confronted with moderators of operational performance. To better model visual detection and identification processes in realistic performance situations, we have constructed an agent-based model of visual perception based on signal detection theory that can be moderated by exigent processes, such as stress and fatigue.
Charles River Analytics has introduced the Dynamic Representation for Evaluating the Effect of Moderators on Stress (DREEMS) project. DREEMS models Warfighter performance through the use of situational awareness modeling. Agent performance is represented in behavior trees using a language called Hap (Loyall & Bates 1991). DREEMS models behavior generation as the cumulative result of an information processing module feeding into a situation module, which
then guide’s an agent’s behavior via goals and behaviors in Hap.
This material is based upon work supported by the US Army Command Center, Aberdeen Proving Ground, Natick Contracting Division ACC-APG-NCD under Contract No. W911QY-17-C-0009. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the US Army Command Center.
1 Charles River Analytics
2 Georgia Tech
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