Publication - An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

Authors: Zhang, X.Shelley; Yoon, S.; DiBona, P.; Appling, D.S.; Ding, L.; Doppa, J.R.; Greeny, D.; Guo, J.K.; Kuter, U.; Levine, G.; MacTavish, R.L.; McFarlane, D.; Michaelis, J.R.; Mostafa, Hala; Ontañón, S.; Parker, C.; Radhakrishnan, J.; Rebguns, A.; Shrestha, B.; Song, Z.; Trewhitt, E.B.; Zafar, Huzaifa; Zhang, Chongjie; Corkill, Daniel; DeJong, G.; Dietterich, T.G.; Kambhampati, S.; Lesser, Victor; et al
Title: An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration
Abstract: We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of integrated learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During the performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. We describe the application of this novel learning and problem-solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.
Keywords: Learning
Publication: ACM Transactions on Intelligent Systems and Technology (TIST), Vol: 3, Num: 4, Article 75, pp. 1 - 38
Publisher: ACM
Date: September 2012
Sources: PDF: /Documents/lesser/XZhang_TIST_a75.pdf
Reference: Zhang, X.Shelley; Yoon, S.; DiBona, P.; Appling, D.S.; Ding, L.; Doppa, J.R.; Greeny, D.; Guo, J.K.; Kuter, U.; Levine, G.; MacTavish, R.L.; McFarlane, D.; Michaelis, J.R.; Mostafa, Hala; Ontañón, S.; Parker, C.; Radhakrishnan, J.; Rebguns, A.; Shrestha, B.; Song, Z.; Trewhitt, E.B.; Zafar, Huzaifa; Zhang, Chongjie; Corkill, Daniel; DeJong, G.; Dietterich, T.G.; Kambhampati, S.; Lesser, Victor; et al. An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration. ACM Transactions on Intelligent Systems and Technology (TIST), Volume 3, Number 4, Article 75, ACM, pp. 1-38. September 2012.
bibtex: 
@article{Zhang-514,
  author    = "X.Shelley Zhang and S. Yoon and P. DiBona and D.S.
               Appling and L. Ding and J.R. Doppa and D. Greeny
               and J.K. Guo and U. Kuter and G. Levine and R.L.
               MacTavish and D. McFarlane and J.R. Michaelis and
               Hala Mostafa and S. Ontañón and C. Parker and J.
               Radhakrishnan and A. Rebguns and B. Shrestha and
               Z. Song and E.B. Trewhitt and Huzaifa Zafar and
               Chongjie Zhang and Daniel Corkill and G. DeJong
               and T.G. Dietterich and S. Kambhampati and Victor
               Lesser and et al",
  title     = "{An Ensemble Architecture for Learning Complex
               Problem-Solving Techniques from Demonstration}",
  journal   = "ACM Transactions on Intelligent Systems and
               Technology (TIST)",
  volume    = "3",
  number    = "4, Article 75",
  publisher = "ACM",
  pages     = "1-38",
  month     = "September",
  year      = "2012",
  url       = "http://mas.cs.umass.edu/paper/514",
}