Control mechanisms and local perception to support autonomous behavior in virtual animated agents

We propose basic mechanisms that can be used as support to perform computer animated agents with autonomous behavior. We provide the agents with local perception and use a control theory approach, dealing with dynamics and kinematics issues, in order to establish a well structured way to control the agent resources. While maintaining the virtual agents with physical specifications and constraints necessary for animation purposes, the mechanisms provide other realistic needs necessary for providing autonomous behavior. In this way, agents can interact with their environment independent of user interaction, learning the characteristics of its habitat and adapting to it in order to survive. We validate the mechanisms by presenting two platforms with different structures (sensors, actuators, and dynamics) which have used them. As practical results, by using the same control structure, the animated agents are able to perform different tasks like learning attention control and pattern categorization based on multi-modal sensory information, learning how to progress onto a rough terrain, and learning how to perform visual monitoring tasks of their environment.