Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB

✍ Scribed by Jun Tani; Masato Ito; Yuuya Sugita

• Publisher: Elsevier Science
• Year: 2004
• Tongue: English
• Weight: 679 KB
• Volume: 17
• Category: Article
• ISSN: 0893-6080
• DOI: 10.1016/j.neunet.2004.05.007

✦ Synopsis

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.