Continuous Decision Diagrams for Machine Learning and Decision-theoretic AI Planning

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A key challenge in both Machine Learning and Decision-theoretic AI Planning is the inability of existing methods to efficiently and accurately reason about piecewise continuous functions. Such functions arise in diverse tasks such as preference learning and real-time optimization of traffic signals. For example, in the latter application area, optimal planners must reason about piecewise continuous bursts of traffic flow that occur when signals change. The proposed research program directly attacks this challenge through the further development of continuous decision diagrams and their application to problems ranging from preference learning and elicitation critical for online commerce to optimized traffic signal control critical for highly congested urban environments.