An ensemble learning and problem solving architecture for airspace management

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1. Xiaoqin (Shelley) Zhang, Sungwook Yoon, Phillip DiBona, Darren Scott Appling, Li Ding, Janardhan Rao Doppa, Derek Greeny, Jinhong K. Guo, Ugur Kuter, Geoff Levine, Reid L. MacTavish, Daniel McFarlane, James Michaelis, Hala Mostafa, Santiago Ontanon, Charles Parker, Jainarayan Radhakrishnan, Anton Rebgunsy, Bhavesh Shrestha, Zhexuan Song, Ethan B. Trewhitt, Huzaifa Zafar, Chongjie Zhang, Daniel Corkill, Gerald DeJong, Thomas G. Dietterich, Subbarao Kambhampati, Victor Lesser, Deborah L. McGuinness, Ashwin Ram, Diana Spearsy, Prasad Tadepalli, Elizabeth T. Whitaker, Weng-Keen Wong, James A. Hendler, Martin O. Hofmann, Kenneth Whitebread. An Ensemble Learning and Problem Solving Architecture for Airspace Management , IAAI'2009 (The Twenty-first IAAI Conference on Artificial Intelligence will be held in Pasadena, California, July 14–16, 2009), 2009

bibtex

@inproceedings { zhang2009an ,
  author = "Xiaoqin (Shelley) Zhang, Sungwook Yoon, Phillip DiBona, Darren Scott Appling, Li Ding, Janardhan Rao Doppa, Derek Greeny, Jinhong K. Guo, Ugur Kuter, Geoff Levine, Reid L. MacTavish, Daniel McFarlane, James Michaelis, Hala Mostafa, Santiago Ontanon, Charles Parker, Jainarayan Radhakrishnan, Anton Rebgunsy, Bhavesh Shrestha, Zhexuan Song, Ethan B. Trewhitt, Huzaifa Zafar, Chongjie Zhang, Daniel Corkill, Gerald DeJong, Thomas G. Dietterich, Subbarao Kambhampati, Victor Lesser, Deborah L. McGuinness, Ashwin Ram, Diana Spearsy, Prasad Tadepalli, Elizabeth T. Whitaker, Weng-Keen Wong, James A. Hendler, Martin O. Hofmann, Kenneth Whitebread", 
booktitle = "IAAI'2009", 
note = "The Twenty-first IAAI Conference on Artificial Intelligence will be held in Pasadena, California, July 14–16, 2009", 
title = "An Ensemble Learning and Problem Solving Architecture for Airspace Management", 
year = "2009", 
} 

abstract: 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.

download:

• paper: TW-2009-10.pdf
• slides: