Enhancements of Systems Based on Bayesian Networks and Structural Equation Models for Command and Control Support
Abstract
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) two state of the art stochastic sampling algorithm for approximate inference in graphical models, both (2) analysis of problems related to combining probabilistic information, (3) an module for interactive construction of causal graphical models and search for opportunities, (4) algorithm for learning graphical models from small data sets, and (5) a prototype of the system, used by over 5,000 people world-wide.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 20, 2000
- Accession Number
- ADA419906
Entities
People
- Marek J. Druzdzel
Organizations
- University of Pittsburgh