  In this paper, we address the anomaly detection problem where the objective
is to find the anomalous processes among a given set of processes. To this end,
the decision-making agent probes a subset of processes at every time instant
and obtains a potentially erroneous estimate of the binary variable which
indicates whether or not the corresponding process is anomalous. The agent
continues to probe the processes until it obtains a sufficient number of
measurements to reliably identify the anomalous processes. In this context, we
develop a sequential selection algorithm that decides which processes to be
probed at every instant to detect the anomalies with an accuracy exceeding a
desired value while minimizing the delay in making the decision and the total
number of measurements taken. Our algorithm is based on active inference which
is a general framework to make sequential decisions in order to maximize the
notion of free energy. We define the free energy using the objectives of the
selection policy and implement the active inference framework using a deep
neural network approximation. Using numerical experiments, we compare our
algorithm with the state-of-the-art method based on deep actor-critic
reinforcement learning and demonstrate the superior performance of our
algorithm.
