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Proceedings Paper

Adapting robot behavior to a nonstationary environment: a deeper biologically inspired model of neural processing
Author(s): George E. Mobus
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Paper Abstract

Biological inspiration admits to degrees. This paper describes a new neural processing algorithm inspired by a deeper understanding of the workings of real biological synapses. It is shown that multi-time domain adaptation approach to encoding casual correlation solves the destructive interference problem encountered by more commonly used learning algorithms. It is also shown how this allows an agent to adapt to nonstationary environment in which longer-term changes in the statistical properties occur and are inherently unpredictable, yet not completely lose useful prior knowledge. Finally, it sis suggested that the use of causal correlation coupled with value-based learning may provide pragmatic solutions to some other classical problems in machine learning.

Paper Details

Date Published: 16 October 2000
PDF: 15 pages
Proc. SPIE 4196, Sensor Fusion and Decentralized Control in Robotic Systems III, (16 October 2000); doi: 10.1117/12.403709
Show Author Affiliations
George E. Mobus, Western Washington Univ. (United States)


Published in SPIE Proceedings Vol. 4196:
Sensor Fusion and Decentralized Control in Robotic Systems III
Gerard T. McKee; Paul S. Schenker, Editor(s)

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