Adapting robot behavior to a nonstationary environment: a deeper biologically inspired model of neural processing

George E. Mobus

doi:10.1117/12.403709

16 October 2000 Adapting robot behavior to a nonstationary environment: a deeper biologically inspired model of neural processing

George E. Mobus

Author Affiliations +

Proceedings Volume 4196, Sensor Fusion and Decentralized Control in Robotic Systems III; (2000) https://doi.org/10.1117/12.403709
Event: Intelligent Systems and Smart Manufacturing, 2000, Boston, MA, United States

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.

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George E. Mobus "Adapting robot behavior to a nonstationary environment: a deeper biologically inspired model of neural processing", Proc. SPIE 4196, Sensor Fusion and Decentralized Control in Robotic Systems III, (16 October 2000); https://doi.org/10.1117/12.403709

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