  Lifelong development allows animals and machines to adapt to changes in the
environment as well as in their own systems, such as wear and tear in sensors
and actuators. An important use case of such adaptation is industrial
odor-sensing. Metal-oxide-based sensors can be used to detect gaseous compounds
in the air; however, the gases interact with the sensors, causing their
responses to change over time in a process called sensor drift. Sensor drift is
irreversible and requires frequent recalibration with additional data. This
paper demonstrates that an adaptive system that represents the drift as context
for the skill of odor sensing achieves the same goal automatically. After it is
trained on the history of changes, a neural network predicts future contexts,
allowing the context+skill sensing system to adapt to sensor drift. Evaluated
on an industrial dataset of gas-sensor drift, the approach performed better
than standard drift-naive and ensembling methods. In this way, the
context+skill system emulates the natural ability of animal olfaction systems
to adapt to a changing world, and demonstrates how it can be effective in
real-world applications.
