title: Biomimetic approach to tacit learning based on compound control.
abstract: The remarkable capability of living organisms to adapt to unknown environments is due to learning mechanisms that are totally different from the current artificial machine-learning paradigm. Computational media composed of identical elements that have simple activity rules play a major role in biological control, such as the activities of neurons in brains and the molecular interactions in intracellular control. As a result of integrations of the individual activities of the computational media, new behavioral patterns emerge to adapt to changing environments. We previously implemented this feature of biological controls in a form of machine learning and succeeded to realize bipedal walking without the robot model or trajectory planning. Despite the success of bipedal walking, it was a puzzle as to why the individual activities of the computational media could achieve the global behavior. In this paper, we answer this question by taking a statistical approach that connects the individual activities of computational media to global network behaviors. We show that the individual activities can generate optimized behaviors from a particular global viewpoint, i.e., autonomous rhythm generation and learning of balanced postures, without using global performance indices.
authors: RIKEN Brain Science Institute (BSI)-Toyota Collaboration Center, Nagoya 463-0003, Japan. shimoda@brain.riken.jp
authors: Shingo
authors: S
authors: Shimoda
authors: Hidenori
authors: H
authors: Kimura
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D016000:Cluster Analysis; D003491:Cybernetics / Q000379:methods*; D016571:Neural Networks, Computer