Finding ways to intercept illicit nuclear materials and weapons destined for the U.S. via the maritime transportation system is an exceedingly difficult task. Today, only a small percentage of ships entering U.S. ports have their cargoes inspected. The purpose of this study is to develop decision support algorithms that will help us to optimally intercept illicit materials and weapons. The algorithms we seek will find inspection schemes that minimize total cost, including ''cost'' of false positives and false negatives.

We envision a stream of entities arriving at a port and a decision maker having to decide how to inspect them, which to subject to further inspection and which to allow to pass through with only minimal levels of inspection. This is a complex sequential decision making problem. Our approach to this problem involves decision logics and is built around problem formulations that lead to the need for combinatorial optimization algorithms as well as methods from the theory of boolean functions, queueing theory, and machine learning. 

The project will be carried out in collaboration between a DIMACS team and a team from the Los Alamos National Laboratory and will initially follow an approach pioneered at Los Alamos.