An ensemble learning and problem solving architecture for airspace management

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An ensemble learning and problem solving architecture for airspace management + Has identifier

Bibtype inproceedings

Has publishing details 2009

Has title An Ensemble Learning and Problem Solving Architecture for Airspace Management

Has tr id TW-2009-10

Has where published IAAI'2009

Has year 2009

Title An Ensemble Learning and Problem Solving Architecture for Airspace Management

Year 2009

Abstract In this paper we describe the application … 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. itecture for learning and problem solving.

Note the Twenty-first IAAI Conference on Artificial Intelligence will be held in Pasadena, California, July 14–16, 2009