Several games nowadays try to improve the player immersion by representing human behavior as real as possible, generally using agent technologies to model non-player characters (NPCs). However, agent-based behavioral models representing the existing complexity of, for instance, a decision-making for a real life situation can become a very intensive computing task. For this reason, real-time simulation-based games may benefit from optimizations produced on how NPCs react to changes in the simulated game world. This paper presents an approach for speeding up the decision-making of autonomous agents representing NPCs of a game. The optimization is reached by bounding the agent perception to a subset of all agent surrounding elements, which contains only the most important elements for the agent at current time. In other words, the agent is modeled as having "focus of attention". The attention focus represented in this work is based on theories of emotions and personality.
['Alberto Signoretti', 'Antonino Feitosa', 'André M. C. Campos', 'Anne M. P. Canuto', 'Sergio V. Fialho']
Increasing the Eciency of NPCs Using a Focus of Attention Based on Emotions and Personality