A neural approach to an attentive navigation for 3D intelligent virtual agents

This paper presents an attempt to integrate attention and navigation skills in 3D embodied agents (virtual humanoids). The neural model presented has been divided in two main phases. Firstly the environment-categorization phase, where an online pattern recognition and categorization of the agent current sensory input data is carried out by an adaptive resonance driven self organizing neural network, which will finally simulate the agent´s short term memory (STM). Secondly, the model must also learn how and when to map its current STM state into the navigation and attention motor layers of the 3D agent. We also review the world modelling and the agent vision system, and finally we present the first results extracted from two of the subsystems which conforms the complete neural model, such as, the environment categorization subsystem and the base navigation neural model.