Genetic algorithms produce individual robotic rat pup behaviors that match Norway rat pup behaviors at multiple scales
We designed input/output cognitive architectures for individual robotic rat pups using genetic algorithms with the aim of achieving insight into Norway rat pup behavior. Our cognitive architectures were evolved using only metrics of Norway rat pup macro-behavior (e.g., percent of time spent in corners, along walls, and center of an arena during animal experiments). Robotic rat pups not only quantitatively matched Norway rat pups at the macro-level (by design), but additionally qualitatively matched Norway rat pup behavior at the micro-behavior scales (corner- snooping, punting). The complexity of the resulting deterministic inputoutput controllers may lend support to previous studies that show random-like control codes (possibly emerging from complex underlying inputoutput interactions, or brain-initiated spontaneous behavior) can produce apparently realistic rat pup behavior below a certain age.
genetic algorithm behavior biorobotics