Active audition using the parameter-less self-organising map
Autonomous Robots
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To understand the behavior of natural autonomous systems, research is carried out on artificial autonomous agents. This paper focuses on how simple behaviors can be learnt autonomously using a bootstrapping method. Firstly, a two dimensional Self-Organizing Map is realized which provides the agent's sense of orientation. Once this relative positioning system has been established, the agent learns to navigate towards a target using the reinforcement learning technique of Q-Learning. Since only neural network processing is used, this technique emulates the distributed and adaptive information processing found in natural autonomous systems. Furthermore, due to its generality, the neural implementation developed is transferable to other artificial autonomous agents with different sensors and effector suites.