An architecture for behaviour coordination learning

This paper describes a neural architecture for learning coordination of different behaviours in a situated agent. Behaviour-oriented approaches define the control of an agent directly in terms of its tasks. A key challenge is how to manage the agent´s ongoing tasks so that action conflict is minimized and the desired levels of compliance with overall goals are achieved. We present mechanisms for adapting the coordination strategy through short- and long-term adaptive inhibition and time-varying performance feedback. Finally, we present preliminary experimental results for a simulated robot which demonstrate the effectiveness of this method