A simple mechanism that can be used to ``learn'' parameter values over time is the digital low-pass filter. A simple digital low-pass filter is defined by:
outi+1 = &alpha * ini + (1 - &alpha) * outi, where &alpha < < 1.
This filter will remove most short-term ``noise'' in the input, while passing through the long-term trend.
A filter like this was used to adjust weights assigned to heuristic feature detectors in Samuel's checker-player program.
An advantage of such a mechanism is that multiple parameter values can be ``learned'' simultaneously, despite the fact that the system's performance changes as the parameter values change.
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