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Proceedings Paper

Communal learning within a distributed robotic control system
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Paper Abstract

It is accepted that the ability to learn and adapt is key to prosperity and survival in both individuals and societies. The same is true of populations of robots. Those robots within a population that are able to learn will outperform, survive longer and perhaps exploit their non-learning co- workers. This paper describes the ongoing results of Communal Learning in the Cognitive Colonies Project (CMU/Robotics and DRES), funded jointly by DARPA ITO- Software for Distributed Robotics and DRDC-DRES. Discussed will be how communal learning fits into the free market architecture for distributed control. Techniques for representing experiences, learned behaviors, maps and computational resources as commodities within the market economy will be presented. Once in a commodity structure, the cycle of speculate, act, receive profits or sustain losses and then learn of the market economy. This allows successful control strategies to emerge and the individuals who discovered them to become established as successful. This paper will discuss: learning to predict costs and make better deals, learning transition confidences, learning causes of death, learning with robot sacrifice and learning model parameters.

Paper Details

Date Published: 20 September 2001
PDF: 10 pages
Proc. SPIE 4364, Unmanned Ground Vehicle Technology III, (20 September 2001); doi: 10.1117/12.440002
Show Author Affiliations
Bruce Leonard Digney, Defence Research Establishment Suffield (Canada)


Published in SPIE Proceedings Vol. 4364:
Unmanned Ground Vehicle Technology III
Grant R. Gerhart; Chuck M. Shoemaker, Editor(s)

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