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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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 Jose Luis Contreras-Vidal David W Jacobs Scott Weems Waseem Naqvi Ransom Winder Timur Chabuk Jin Hyuk Jung Changjiang Yang

Citations