David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Minds and Machines 15 (2):131-181 (2005)
The well-known game of chess has traditionally been modeled in artificial intelligence studies by search engines with advanced pruning techniques. The models were thus centered on an inference engine manipulating passive symbols in the form of tokens. It is beyond doubt, however, that human players do not carry out such processes. Instead, chess masters instead carry out perceptual processes, carefully categorizing the chunks perceived in a position and gradually building complex dynamic structures to represent the subtle pressures embedded in the positions. In this paper we will consider two hypotheses concerning the underlying subcognitive processes and architecture. In the first hypothesis, a multiple-leveled chess representational structure is presented, which includes distance graphs (with varying levels of quality) between pieces, piece mobilities, and abstract roles. These representational schemes seem to account for numerous characteristics of human player’s psychology. The second hypothesis concerns the extension of the architecture proposed in the Copycat project as central for modeling the emergent intuitive perception of a chess position. We provide a synthesis on how the postulated architecture models chess intuition as an emergent mixture of simultaneous distance estimations, chunk perceptions, abstract role awareness, and intention activations. This is an alternative model to the traditional AI approaches, focusing on the philosophy of active symbols.
|Keywords||active symbols artificial intelligence chess cognitive modeling psychology of intuition|
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Allen Newell (1980). Physical Symbol Systems. Cognitive Science 4 (2):135-83.
F. Gobet, P. Lane, S. Croker, P. Cheng, G. Jones, I. OlIver & J. Pine (2001). Chunking Mechanisms in Human Learning. Trends in Cognitive Sciences 5 (6):236-243.
Fernand Gobet & Herbert A. Simon (2000). Five Seconds or Sixty? Presentation Time in Expert Memory. Cognitive Science 24 (4):651-682.
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Citations of this work BETA
Marcelo S. Brogliato, Daniel M. Chada & Alexandre Linhares (2014). Sparse Distributed Memory: Understanding the Speed and Robustness of Expert Memory. Frontiers in Human Neuroscience 8.
Fernand Gobet & Philippe Chassy (2009). Expertise and Intuition: A Tale of Three Theories. [REVIEW] Minds and Machines 19 (2):151-180.
Merim Bilalić, Peter McLeod & Fernand Gobet (2009). Specialization Effect and Its Influence on Memory and Problem Solving in Expert Chess Players. Cognitive Science 33 (6):1117-1143.
Merim Bilalić & Fernand Gobet (2009). They Do What They Are Told to Do: The Influence of Instruction on (Chess) Expert Perception—Commentary on Linhares and Brum (2007). Cognitive Science 33 (5):743-747.
Alexandre Linhares (2008). Dynamic Sets of Potentially Interchangeable Connotations: A Theory of Mental Objects. Behavioral and Brain Sciences 31 (4):389-390.
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