ARBIB: An autonomous robot based on inspirations from biology
Simple artificial creatures (animats), which operate as autonomous, adaptive robots in the real world, can serve both as models of biology and as a radical alternative to conventional methods of designing intelligent systems. We describe the evolution and implementation of the autonomous robot ARBIB, which learns from and adapts to its environment. A primary goal was to test the notion that effective robot learning can be based on neural habituation and sensitization, so validating the suggestion of Hawkins and Kandel that (associative) classical and higher-order conditioning might be based on an elaboration of these (non-associative) forms of learning. Accordingly, ARBIBs nervous system has a non-homogeneous population of spiking neurons, and learning is by modification of basic, pre-existing (hard-wired) reflexes. By monitoring firing rates of specific neurons and synaptic weights between neural connections as ARBIB learns from its environment, we confirm that both classical and higher-order conditioning occur, leading to the emergence of interesting and ecologically valid behaviors.
higher-order conditioning nervous system biologically-based robotics classical conditioning