CAREER: A Decision-Theoretic Approach to Intelligent Planning and Control

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This is the first year of funding of a 4-year continuing award. This project adopts a decision theoretic approach to problems of intelligent planning and control, especially problems that are characterized by uncertainty and imperfect information. One focus of this research is the development of improved algorithms for problems that are formalized as Markov decision processes and/or influence diagrams. An innovative approach is adopted in which plans (or policies) are represented as finite automata, and classic AI search and planning techniques for finding sequential plans are generalized to find more complex conditional plans that take the form of automata. This should lead to more efficient planning algorithms by exploiting problem structure in the form of factored representations, as well as hierarchical and distributed problem decomposition. A complementary focus of this project is the application of decision-theoretic techniques to, practical problems, including planning the process of knowledge discovery in a large oceanographic image database, and dynamic load balancing of scientific computations in a parallel, distributed memory environment. This research will result in more efficient algorithms for decision-theoretic planning and control that can solve larger and more complex problems. It will also result in an improved understanding of how to apply these algorithms and models to practical applications.