]> 2012-02-11T04:16:19-05:00 An ensemble learning and problem solving architecture for airspace management 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. inproceedings IAAI'2009 zhang2009an An Ensemble Learning and Problem Solving Architecture for Airspace Management TW-2009-10 2009 Year Tr id Title Computer science Tag Media:TW-2009-10.pdf Paper The Twenty-first IAAI Conference on Artificial Intelligence will be held in Pasadena, California, July 14–16, 2009 Note Key Booktitle Bibtype Kenneth Whitebread Martin O. Hofmann James A. Hendler Weng-Keen Wong Elizabeth T. Whitaker Prasad Tadepalli Diana Spearsy Ashwin Ram Deborah L. McGuinness Victor Lesser Subbarao Kambhampati Thomas G. Dietterich Gerald DeJong Daniel Corkill Chongjie Zhang Huzaifa Zafar Ethan B. Trewhitt Zhexuan Song Bhavesh Shrestha Anton Rebgunsy Jainarayan Radhakrishnan Charles Parker Santiago Ontanon Hala Mostafa James Michaelis Daniel McFarlane Reid L. MacTavish Geoff Levine Ugur Kuter Jinhong K. Guo Derek Greeny Janardhan Rao Doppa Li Ding Darren Scott Appling Phillip DiBona Sungwook Yoon Xiaoqin (Shelley) Zhang Author Abstract TW Technical Report Publication Proceeding Paper Conference Paper