Information integration via hierarchical and hybrid bayesian networks

Abstract — A collaboration scheme for information integration among multiple agencies (and/or various divisions within a single agency) is designed using hierarchical and hybrid Bayesian networks (HHBNs). In this scheme, raw information is represented by transactions (such as communication, travel, financing), and information entities to be integrated are modeled as random variables (such as: an event occurs, an effect exists, or an action is undertaken). Each random variable has certain states with probabilities assigned to them. Hierarchical is in terms of the model structure and hybrid stems from our usage of both general Bayesian networks (BNs) and hidden Markov models (HMMs, a special form of dynamic BNs). The general Bayesian networks are adopted in the top (decision) layer to address global assessment for a specific question (e.g., “Is target A under terrorist threat?” in the context of counter-terrorism). HMMs function in the bottom (observation) layer to report processed evidence to the upper layer BN based on the local information available to a particular agency or a division. A software tool, termed the adaptive safety analysis and monitoring (ASAM) system, is developed to implement HHBNs for information integration either in a centralized or in a distributed fashion. As an example, a terrorist attack scenario gleaned from open sources is modeled and analyzed to illustrate the functionality of the proposed framework. Index Terms — Information integration, decision making, hidden Markov models, Bayesian networks, counter-terrorism