Intelligent motor decision: From selective attention to a Bayesian world model

Intelligent sensor/motor allocation is gaining in importance in many areas of robotics and autonomous systems. It allows the autonomous entity to allocate its resources for solving the currently most critical task depending on the entitypsilas current state, its sensory input and its acquired knowledge of the world. Such architectures which support dynamic motor allocation are invaluable for systems with limited resources. Biological systems also build and maintain a world-model to enable intelligent motor decision making. Based on recent advances in attention research and psychophysiology we propose a general purpose push-pull selective attention mechanism for building a world model and intelligent motor action control. We implement and test an architecture called A-BID, which is guided by a neural network implementation of a selective attention mechanism that is used to build a probabilistic world model. Using A-BID, the system performs at each time step the action that is optimal in the Bayesian sense.