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Simulations based on cognitively rich agents can become a very intensive computing task, especially when the simulated world represents a complex system. Those simulations can however benefit from optimizations coming from the way in which agents react to changes in the simulated environment. This paper presents an approach for improving the efficiency of the decision-making process of autonomous agents in a simulation. The optimization is reached by dynamically adapting the agent's perception to a bounded subset of all the agent's surrounding elements, which contains only the most important elements for the agent at the current time. In other words, the agent is modeled as having a dynamic focus of attention.