An Ensemble Learning and Problem Solving Architecture for Airspace Management

Zhang, Xiaoqin (Shelly) (University of Massachusetts) | Yoon, Sungwook (Arizona State University) | DiBona, Phillip (Lockheed Martin  Advanced Technology Laboratories) | Appling, Darren (Georgia Institute of Technology) | Ding, Li (Rensselaer Polytechnic Institute) | Doppa, Janardhan (Oregon State University) | Green, Derek (University of Wyoming) | Guo, Jinhong (Lockheed Martin Advanced Technology Laboratories) | Kuter, Ugur (University of Maryland) | Levine, Geoff (University of Illinois at Urbana) | MacTavish, Reid (Georgia Institute of Technology) | McFarlane, Daniel (Lockheed Martin Advanced Technology Laboratories) | Michaelis, James (Rensselaer Polytechnic Institute) | Mostafa, Hala (University of Massachusetts) | Ontanon, Santiago (Georgia Institute of Technology) | Parker, Charles (Georgia Institute of Technology) | Radhakrishnan, Jainarayan (University of Wyoming) | Rebguns, Anton (University of Massachusetts) | Shrestha, Bhavesh (Fujitsu Laboratories of America) | Song, Zhexuan (Georgia Institute of Technology) | Trewhitt, Ethan (University of Massachusetts) | Zafar, Huzaifa (University of Massachusetts) | Zhang, Chongjie (University of Massachusetts) | Corkill, Daniel (University of Illinois at Urbana-Champaign) | DeJong, Gerald (Oregon State University) | Dietterich, Thomas (Arizona State University) | Kambhampati, Subbarao (University of Massachusetts) | Lesser, Victor (Rensselaer Polytechnic Institute) | McGuinness, Deborah L. (Georgia Institute of Technology) | Ram, Ashwin (University of Wyoming) | Spears, Diana (Oregon State University) | Tadepalli, Prasad (Georgia Institute of Technology) | Whitaker, Elizabeth (Oregon State University) | Wong, Weng-Keen (Rensselaer Polytechnic Institute) | Hendler, James (Lockheed Martin Advanced Technology Laboratories) | Hofmann, Martin (Lockheed Martin Advanced Technology Laboratories) | Whitebread, Kenneth

AAAI Conferences 

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.