Systems Based on Bayesian Belief Networks and Structural Equation Models for Command and Control Support

摘要

The performed project focused on a new paradigm of planning systems that are based on a combination of Bayesian networks and structural equation models. We focused on theoretical issues that surround combining the two in a practical planning system, developing the foundations for, and building a prototype of such system. The approach and the system built allow for efficient, yet normatively correct, treatment of various types of information, uncertainty, and utility. It is especially powerful in complex situations where the available information is heterogeneous and consists of a mixture of deterministic and uncertain relationships among discrete and continuous variables. Our main contributions are: (1) several fast state of the art stochastic sampling algorithms for approximate inference in graphical models, (2) treatment of reversible causal mechanisms for causal reasoning in graphical models, (3) a scheme for interactive construction of causal graphical models based on causal mechanisms, (4) an algorithm for learning graphical models from data, and (5) a prototype of the system, used by over 2,300 people world-wide.