Cost-Aware Sequential Bayesian Tasking and Decision-Making for Search and Classification

This paper focuses on the development of a cost-aware sequential Bayesian decision-making strategy for the search and classification of multiple unknown objects within a task domain. Search and classification of multiple objects of unknown numbers are competing tasks under limited vehicle and sensory resources. This is because sensor-equipped vehicles in the system can perform either the search or classification task but not both at the same time. The decision of one task over the other may result in missing other, more important objects not yet found or missing the opportunity to classify a found critical object. In this paper we develop a cost-aware sequential Bayesian decision-making strategy for search and classification, which results in the detection and satisfactory classification of all the unknown objects in the task domain.