Now showing items 1-3 of 3

  • Deep active learning for autonomous navigation. 

    Hussein, Ahmed; Gaber, Mohamed Medhat; Elyan, Eyad (Springer https://doi.org/10.1007/978-3-319-44188-7_1, 2016-08-19)
    HUSSEIN, A., GABER, M.M. and ELYAN, E. 2016. Deep active learning for autonomous navigation. In Jayne, C. and Iliadis, L. (eds.) Communications in computer and information science, 629. Engineering applications of neural networks: proceedings of the 17th International conference on engineering applications of neural networks (EANN 2016), 2 - 5 September 2016, Aberdeen, UK. Cham: Springer [online], pages 3-17. Available from: https://doi.org/10.1007/978-3-319-44188-7_1
    Imitation learning refers to an agent's ability to mimic a desired behavior by learning from bservations. A major challenge facing learning from demonstrations is to represent the demonstrations in a manner that is adequate ...
  • Minds for robots. 

    Macleod, Christopher; Maxwell, Grant M. (St. John Patrick Publishers Ltd., 2009-01)
    MACLEOD, C. and MAXWELL, G. M., 2009. Minds for robots. Electronics World, 115 (1873), pp. 16-19.
    In the 1950s and 60s, popular culture was entranced by robots. There was Robby in “Forbidden Planet,” Gort in “The day the Earth stood still” and many others. This fascination has continued to the present day, only the ...
  • Real time evolutionary algorithms in robotic neural control systems. 

    Jagadeesan, Ananda Prasanna (The Robert Gordon University School of Engineering, 2006)
    This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context “on-line” means while it is in use). Traditionally, Evolutionary Algorithms ...