Abstract
My research interests lie at the intersection of the planning and machine learning areas. My research objectives include the design of new AI planning methods that can improve their performance over time through learning. I am particularly interested in planning tasks as an opportunity for learning, as well as learning as a way to improve planning performance.
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D. Furcy and S. Koenig. Speeding up the convergence of real-time search. In Proceedings of the National Conference on Artificial Intelligence, 2000.
D. Furcy and S. Koenig. Speeding up the convergence of real-time search: Empirical setup and proofs. Technical Report GIT-COGSCI-2000/01, College of Computing, Georgia Institute of Technology, Atlanta (Georgia), 2000.
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Furcy, D. (2000). Using and Learning Abstraction Hierarchies for Planning. In: Choueiry, B.Y., Walsh, T. (eds) Abstraction, Reformulation, and Approximation. SARA 2000. Lecture Notes in Computer Science(), vol 1864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44914-0_24
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DOI: https://doi.org/10.1007/3-540-44914-0_24
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