AAAI Publications, Twenty-First IAAI Conference

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An Ensemble Learning and Problem Solving Architecture for Airspace Management
Xiaoqin (Shelly) Zhang, Sungwook Yoon, Phillip DiBona, Darren Appling, Li Ding, Janardhan Doppa, Derek Green, Jinhong Guo, Ugur Kuter, Geoff Levine, Reid MacTavish, Daniel McFarlane, James Michaelis, Hala Mostafa, Santiago Ontanon, Charles Parker, Jainarayan Radhakrishnan, Anton Rebguns, Bhavesh Shrestha, Zhexuan Song, Ethan Trewhitt, Huzaifa Zafar, Chongjie Zhang, Daniel Corkill, Gerald DeJong, Thomas Dietterich, Subbarao Kambhampati, Victor Lesser, Deborah L. McGuinness, Ashwin Ram, Diana Spears, Prasad Tadepalli, Elizabeth Whitaker, Weng-Keen Wong, James Hendler, Martin Hofmann, Kenneth Whitebread

Last modified: 2009-04-09


In this paper we describe the application of a novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspace need to be reconciled and managed automatically. The key feature of our "Generalized Integrated Learning Architecture" (GILA) is a set of integrated learning and reasoning (ILR) systems coordinated by a central meta-reasoning executive (MRE). Each ILR learns independently from the same training example and contributes to problem-solving in concert with other ILRs as directed by the MRE. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Further, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.


Learning from demonstration; Collaborative performance guided by meta-level controller

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