  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 self-organizing
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.
