Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB.
The current paper reviews a connectionist model, the recurrent neural network with parametric biases (RNNPB), in which multiple behavior schemata can be learned by the network in a distributed manner. The parametric biases in the network play an essential role in both generating and recognizing behavior patterns. They act as a mirror system by means of self-organizing adequate memory structures. Three different robot experiments are reviewed: robot and user interactions; learning and generating different types of dynamic patterns; and linguistic-behavior binding. The hallmark of this study is explaining how self-organizing internal structures can contribute to generalization in learning, and diversity in behavior generation, in the proposed distributed representation scheme.
Tani
Jun
J
Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan. tani@brain.riken.go.jp
Ito
Masato
M
Sugita
Yuuya
Y
D001185:Artificial Intelligence; D001519:Behavior; D008037:Linguistics; D008568:Memory; D009068:Movement; D016571:Neural Networks, Computer; D009474:Neurons; D010364:Pattern Recognition, Visual; D012371:Robotics / Q000295:instrumentation* / Q000379:methods*