  In this work, we study the problem of actively classifying the attributes of
dynamical systems characterized as a finite set of Markov decision process
(MDP) models. We are interested in finding strategies that actively interact
with the dynamical system and observe its reactions so that the attribute of
interest is classified efficiently with high confidence. We present a
decision-theoretic framework based on partially observable Markov decision
processes (POMDPs). The proposed framework relies on assigning a classification
belief (a probability distribution) to the attributes of interest. Given an
initial belief, a confidence level over which a classification decision can be
made, a cost bound, safe belief sets, and a finite time horizon, we compute
POMDP strategies leading to classification decisions. We present three
different algorithms to compute such strategies. The first algorithm computes
the optimal strategy exactly by value iteration. To overcome the computational
complexity of computing the exact solutions, we propose a second algorithm
based on adaptive sampling and a third based on a Monte Carlo tree search to
approximate the optimal probability of reaching a classification decision. We
illustrate the proposed methodology using examples from medical diagnosis,
security surveillance, and wildlife classification.
