Biologically-motivated neural learning in situated systems

We describe the autonomous robot ARBIB. This uses biologically-motivated forms of learning to adapt to its environment. ARBIB´S `nervous system´ has a non-homogeneous population of spiking neurons, and uses both nonassociative and associative forms of learning to modify pre-existing (`hard-wired´) reflexes. As a result of interaction with its environment, interesting and `intelligent´ light-seeking and collision-avoidance behaviors emerge which were not pre-programmed into the robot (or `animat´). These behaviors are similar to those described by other workers who have generally used behaviorally-motivated reinforcement learning rather than biologically-based associative learning. The complexity of observed behavior is remarkable given the extreme simplicity of ARBIB´s `nervous system´, having just 33 neurons. We take this to indicate that great potential exists to explore further “the animat path to AI”