Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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 heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are  akly coupled  in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During 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. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic.  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 ll as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the por of the ensemble architecture for learning and problem solving.
['Xiaoqin Shelley Zhang', 'Bhavesh Shrestha', 'Sungwook Yoon', 'Subbarao Kambhampati', 'Phillip Dibona', 'Jinhong K. Guo', 'Daniel C. McFarlane', 'Martin O. Hofmann', 'Kenneth R. Whitebread', 'Darren Scott Appling', 'Elizabeth T. Whitaker', 'Ethan Trewhitt', 'Li Ding', 'James R. Michaelis', 'Deborah L. McGuinness', 'James A. Hendler', 'Janardhan Rao Doppa', 'Charles Parker', 'Thomas G. Dietterich', 'Prasad Tadepalli', 'Weng Keen Wong', 'Derek Green', 'Anton Rebguns', 'Diana F. Spears', 'Ugur Kuter', 'Geoff Levine', 'Gerald DeJong', 'Reid MacTavish', 'Santiago Ontañón', 'Jainarayan Radhakrishnan', 'Ashwin Ram', 'Hala Mostafa', 'Huzaifa Zafar', 'Chongjie Zhang', 'Daniel D. Corkill', 'Victor R. Lesser', 'Zhexuan Song']
I am writing a research paper on a idea An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration
[{"from": "human", "value": "I am writing a research paper on a idea An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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 \u201cGeneralized Integrated Learning Architecture\u201d (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are  akly coupled  in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During 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. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic.  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 ll as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the por of the ensemble architecture for learning and problem solving."}]