Towards Applying Interactive POMDPs to Real-World Adversary Modeling

We examine the suitability of using decision processes to model real-world systems of intelligent adversaries. Decision processes have long been used to study cooperative multiagent interactions, but their practical applicability to adversarial problems has received minimal study. We address the pros and cons of applying sequential decision-making in this area, using the crime of money laundering as a specific example. Motivated by case studies, we abstract out a model of the money laundering process, using the framework of interactive partially observable Markov decision processes (I-POMDPs). We discuss why this framework is well suited for modeling adversarial interactions. Particle filtering and value iteration are used to solve the model, with the application of different pruning and look-ahead strategies to assess the tradeoffs between solution quality and algorithmic run time. Our results show that there is a large gap in the level of realism that can currently be achieved by such decision models, due to computational demands that limit the size of problems that can be solved. While these results represent solutions to a simplified model of money laundering, they illustrate nonetheless the kinds of agent interactions that cannot be captured by standard approaches such as anomaly detection. This implies that I-POMDP methods may be valuable in the future, when algorithmic capabilities have further evolved.