Active Inference on the Edge: A Design Study
Boris Sedlak Victor Casamayor Pujol Praveen Kumar Donta Schahram Dustdar
Machine Learning (ML) is a common tool to interpret and predict the behavior
of distributed computing systems, e.g., to optimize the task distribution
between devices. As more and more data is created by Internet of Things (IoT)
devices, data processing and ML training are carried out by edge devices in
close proximity. To ensure Quality of Service (QoS) throughout these
operations, systems are supervised and dynamically adapted with the help of ML.
However, as long as ML models are not retrained, they fail to capture gradual
shifts in the variable distribution, leading to an inaccurate view of the
system state. Moreover, as the prediction accuracy decreases, the reporting
device should actively resolve uncertainties to improve the model's precision.
Such a level of self-determination could be provided by Active Inference (ACI)
-- a concept from neuroscience that describes how the brain constantly predicts
and evaluates sensory information to decrease long-term surprise. We
encompassed these concepts in a single action-perception cycle, which we
implemented for distributed agents in a smart manufacturing use case. As a
result, we showed how our ACI agent was able to quickly and traceably solve an
optimization problem while fulfilling QoS requirements.