Development of a Large-Scale Integrated Neurocognitive Architecture - Part 2: Design and Architecture

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@TechReport{oai:drum.umd.edu:1903/3957,
  title =        "Development of a Large-Scale Integrated Neurocognitive
                 Architecture - Part 2: Design and Architecture",
  author =       "J. Reggia and M. Tagamets and J. Contreras-Vidal and 
                 D. Jacobs and S. Weems and W. Naqvi and R. Winder and 
                 T. Chabuk and J. Jung and C. Yang",
  year =         "2006",
  institution =  "University of Maryland",
  number =       "TR-CS-4827, UMIACS-TR-2006-43",
  address =      "USA",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, GPU",
  URL =          "https://drum.umd.edu/dspace/bitstream/1903/3957/1/MarylandPart2.pdf",
  URL =          "http://hdl.handle.net/1903/3957",
  abstract =     "In Part 1 of this report, we outlined a framework for
                 creating an intelligent agent based upon modelling the
                 large-scale functionality of the human brain. Building
                 on those results, we begin Part 2 by specifying the
                 behavioural requirements of a large-scale
                 neurocognitive architecture. The core of our long-term
                 approach remains focused on creating a network of
                 neuromorphic regions that provide the mechanisms needed
                 to meet these requirements. However, for the short term
                 of the next few years, it is likely that optimal
                 results will be obtained by using a hybrid design that
                 also includes symbolic methods from AI/cognitive
                 science and control processes from the field of
                 artificial life. We accordingly propose a three-tiered
                 architecture that integrates these different methods,
                 and describe an ongoing computational study of a
                 prototype 'mini-Roboscout' based on this architecture.
                 We also examine the implications of some non-standard
                 computational methods for developing a neurocognitive
                 agent. This examination included computational
                 experiments assessing the effectiveness of genetic
                 programming as a design tool for recurrent neural
                 networks for sequence processing, and experiments
                 measuring the speed-up obtained for adaptive neural
                 networks when they are executed on a graphical
                 processing unit (GPU) rather than a conventional CPU.
                 We conclude that the implementation of a large-scale
                 neurocognitive architecture is feasible, and outline a
                 roadmap for achieving this goal.",
  bibsource =    "OAI-PMH server at drum.umd.edu",
  format =       "1426146 bytes",
  language =     "en_US",
  oai =          "oai:drum.umd.edu:1903/3957",
  relation =     "UM Computer Science Department; CS-TR-4827; UMIACS;
                 UMIACS-TR-2006-43",
  size =         "32 pages",
}

Genetic Programming entries for James A Reggia M Tagamets J Contreras-Vidal D Jacobs S Weems W Naqvi Ransom Winder T Chabuk Jin Hyuk Jung Changjiang Yang

Citations