CAREER: Explanation, Decision Making, and Learning in Graphical Models

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Graphical models, such as Bayesian networks and influence diagrams, provide principled approaches to solving reasoning and decision making under uncertainty problems. However, the adaptability and scalability of existing methods for these graphical models are often limited. This project aims to address some of these limitations by developing new and improved approaches to explanation, decision making, and learning in graphical models. It includes the following specific objectives: (1) developing new approaches to finding explanations that only contain the most relevant variables for given observations in Bayesian networks, (2) developing heuristic search-based methods and algorithms to solve influence diagrams more efficiently, (3) developing new algorithms for learning optimal Bayesian networks guided by domain-specific heuristic information so that only a small fraction of the solution space need to be explored, and (4) applying the methods developed in this project to real-world applications including multiple-fault diagnosis, supply chain risk management, and online collaborative learning. This project can lead to significantly better approaches to reasoning and decision making under uncertainty in many disciplines where graphical models have found successful applications, including medicine, security, planning, business, economics, education, and many others. This project can also lead to the development of new and enhanced courses and curricula, the involvement of students from underrepresented groups in the research, and a wide dissemination of the research outcomes through free software, publications, and presentations.