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6 results

  • 1 - History, motivations, and core themes

  • Edited by Keith Frankish, The Open University, Milton Keynes, William M. Ramsey, University of Nevada, Las Vegas
  • Book: The Cambridge Handbook of Artificial Intelligence
  • Published online: 05 July 2014
  • Print publication: 12 June 2014, pp 15-33

  • Summary

    This chapter introduces the field of artificial intelligence (AI) through a review of its core themes, history, major research areas, and current trends. It gives a brief history of AI with an account of some of its relatively recent major accomplishments. These include knowledge-based expert systems, chess players, theorem provers, natural language processing, and a new killer application. There are almost a dozen distinct subfields of AI research, each with its own specialized journals, conferences, workshops, and so on. The chapter provides a concise account of the research interests in each of these subfields. Flourishing recent trends include soft computing, AI for data mining, agent-based AI, cognitive computing (including developmental robotics and artificial general intelligence), and the application of AI in cognitive science. Research into artificial intelligence is thriving as never before, and promises continuing contributions, both practical to engineering and theoretical to science.

  • Chapter 7 - Computational models of consciousness: a taxonomy and some examples

  • from Part I - The cognitive science of consciousness
  • Edited by Philip David Zelazo, University of Toronto, Morris Moscovitch, University of Toronto, Evan Thompson, University of York
  • Book: The Cambridge Handbook of Consciousness
  • Published online: 05 June 2012
  • Print publication: 14 May 2007, pp -

  • Summary

    This chapter provides an overview of existing computational (mechanistic) models of cognition in relation to the study of consciousness, on the basis of psychological and philosophical theories and data. It begins by examining some foundational issues concerning computational approaches toward consciousness. Then, various existing models and their explanations of the conscious/unconscious distinction are presented. Work in the area of computational modeling of consciousness generally assumes the sufficiency and the necessity of mechanistic explanations. The chapter looks into some details of two representative computational models, exemplifying either two systems or one-system views. Various related issues, such as the utility of computational models, explanations of psychological data, and potential applications of machine consciousness, have been touched on in the process. Based on existing psychological and philosophical evidence, existing models were compared and contrasted to some extent.

  • Bridging the gap: Dynamics as a unified view of cognition

  • Derek Harter, Arthur C. Graesser, Stan Franklin
  • Journal: Behavioral and Brain Sciences / Volume 24 / Issue 1 / February 2001
  • Published online by Cambridge University Press: 30 October 2001, pp. 45-46

  • Top-down dynamical models of cognitive processes, such as the one presented by Thelen et al., are important pieces in understanding the development of cognitive abilities in humans and biological organisms. Unlike standard symbolic computational approaches to cognition, such dynamical models offer the hope that they can be connected with more bottom-up, neurologically inspired dynamical models to provide a complete view of cognition at all levels. We raise some questions about the details of their simulation and about potential limitations of top-down dynamical models.