Schema-based learning

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Schema-based learning: towards a theory of organization for biologically-inspired autonomous agents

Full text

Pdf (215 KB)

Source

International Conference on Autonomous Agents archive
Proceedings of the first international conference on Autonomous agents table of contents

Marina del Rey, California, United States

Pages: 520 - 521

Year of Publication: 1997

ISBN:0-89791-877-0

Authors

Fernando J. Corbacho	Center for Neural Engineering, University of Southern California, Los Angeles, CA
Michael A. Arbib	Center for Neural Engineering, University of Southern California, Los Angeles, CA

Sponsors

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 15, Citation Count: 1

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/267658.267809 What is a DOI?

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Barto, A. G., Sutton, R. & Anderson, C. W. {1983). Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems. IEEE Trans. Syst., Mall, Cybern., 13(5): 834-846.
	2	Bell, C. Dunn, K., Hall,. C. & Capufi, A. {1995). Electric Organ Corollary Discharge pathways in Mormyrid fish: The Mesencephalic Command associated nucleus. J. Comp. Phys. A, 177(4): 449-462.
	3	Brooks, R. A. (1986). A robust layered control system for a mobile robot. IEEE Trans. Rob. Automation, 2: 14-23.
	4	Fernando J. Corbacho , Michael A. Arbib, Learning to detour, Adaptive Behavior, v.3 n.4, p.419-468, Spring 1995 [doi>10.1177/105971239500300404]
	5	Corbacho, F. & Arbib, M. A. (1996). Learning to Detour and Schemabased Learning. Proceedings of the Fourth lnternatio,al Conference on Simulation of Adaptive Behavior.
	6	Duhamel, J. R., Colby, C. L., & Goldberg, M. E. (1992). The Updating of the Representation of Visual Space in Parietal Cortex by Intended Eye Movements. Science, 255: 90-92.
	7	Hummel, R. A., & Zucker, S. W. (1983) On the Foundations of Relaxation Labelling Processes, IEEE Trans. Pattern Anah,sis and Machine Intelligence, 5:267-287.
	8	Jordan, M. I. & Rumeihart, D. (1992). Forward models: Supervised learning with a distal teacher. Cognitive Science, 16:307-354.
	9	Wei-Min Shen, Autonomous Learning from the Environment, W. H. Freeman & Co., New York, NY, 1994
	10	Thompson, R. F. (1986). The neurobiology of learning and memory. Science, 233: 941-947.