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Journal of Robotics
Volume 2010 (2010), Article ID 523757, 8 pages
Review Article

Deep Blue Cannot Play Checkers: The Need for Generalized Intelligence for Mobile Robots

1U.S. Army Research Laboratory, AMSRD-ARL-HR-SE, Aberdeen Proving Ground, MD 21005-5425, USA
2Department of Engineering and Mathematics, The Pennsylvania State University, University Park, PA 16802, USA

Received 7 November 2009; Accepted 20 March 2010

Academic Editor: Noriyasu Homma

Copyright © 2010 Troy D. Kelley and Lyle N. Long. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Generalized intelligence is much more difficult than originally anticipated when Artificial Intelligence (AI) was first introduced in the early 1960s. Deep Blue, the chess playing supercomputer, was developed to defeat the top rated human chess player and successfully did so by defeating Gary Kasporov in 1997. However, Deep Blue only played chess; it did not play checkers, or any other games. Other examples of AI programs which learned and played games were successful at specific tasks, but generalizing the learned behavior to other domains was not attempted. So the question remains: Why is generalized intelligence so difficult? If complex tasks require a significant amount of development, time and task generalization is not easily accomplished, then a significant amount of effort is going to be required to develop an intelligent system. This approach will require a system of systems approach that uses many AI techniques: neural networks, fuzzy logic, and cognitive architectures.