Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System
Abstract
We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for be- havior 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 lat- eral connectivity. Motor signals modulate this lateral connectivity and a dynamic field on the layer organizes a planning process. All mecha- nisms are local and adaptation is based on Hebbian ideas. The model is continuous in the action, perception, and time domain.
Cite
Text
Toussaint. "Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System." Neural Information Processing Systems, 2003.Markdown
[Toussaint. "Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/toussaint2003neurips-learning/)BibTeX
@inproceedings{toussaint2003neurips-learning,
title = {{Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System}},
author = {Toussaint, Marc},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {926-936},
url = {https://mlanthology.org/neurips/2003/toussaint2003neurips-learning/}
}