Cost-Bounded Active Classification Using Partially Observable Markov Decision Processes
For researchers in sequential decision-making and classification, this provides a principled framework for cost-bounded active classification of dynamical systems, though it is an incremental extension of existing POMDP methods.
This work addresses active classification of dynamical systems modeled by a finite set of MDPs, proposing a POMDP-based framework to design strategies that minimize misclassification under cost constraints. The approach achieves exact optimal strategies via value iteration and approximate solutions via adaptive sampling, demonstrated on medical diagnosis and intruder detection examples.
Active classification, i.e., the sequential decision-making process aimed at data acquisition for classification purposes, arises naturally in many applications, including medical diagnosis, intrusion detection, and object tracking. In this work, we study the problem of actively classifying dynamical systems with 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 true model is determined efficiently with high confidence. To this end, 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 each candidate MDP model. Given an initial belief, some misclassification probabilities, a cost bound, and a finite time horizon, we design POMDP strategies leading to classification decisions. We present two different approaches to find such strategies. The first approach computes the optimal strategy "exactly" using value iteration. To overcome the computational complexity of finding exact solutions, the second approach is based on adaptive sampling to approximate the optimal probability of reaching a classification decision. We illustrate the proposed methodology using two examples from medical diagnosis and intruder detection.