Adapting robot behavior to a nonstationary environment: a deeper biologically inspired model of neural processing
George E. Mobus · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
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.