Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System

We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for behavior planning. State representations are learned with a growing selforganizing layer which is directly coupled to a perception and a motor layer. Knowledge about possible state transitions is encoded in the lateral connectivity. Motor signals modulate this lateral connectivity and a dynamic field on the layer organizes a planning process. All mechanisms are local and adaptation is based on Hebbian ideas. The model is continuous in the action, perception, and time domain.

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Marc Toussaint
• Keywords: Lateral Connectivity, Motor Layer, NIPS 2003, NIPS 2007, Possible State Transitions