43.Pattern Analysis and Machine Intelligence by John G. Webster (Editor)

By John G. Webster (Editor)

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B. InR. B. ), Knowledge-Based 19 Systems in Artificial Intelligence. New York: McGraw-Hill, 1980. 39. B. V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project. Reading, Massachusetts: Addison-Wesley, 1990. 40. -P. Liang andE. Special issue on case-based reasoning and its applications. 41. R. Lucas, Minds, machines, and G¨odel. Philosophy, 36, 1961. 42. M. J. Hayes, Some philosophical problems from the standpoint of artificial intelligence. Machine Intelligence, 4: 463–502, 1969.

Rather than assuming that there were predestinated a priori mental structures that could not be automatically reconstructed, in empirism one assumes that knowledge emerges from observations. Along the extremely reductionistic and simplistic lines of reasoning that are here adopted, the goal is to find the data structures starting from tabula rasa: no specialized data structures exist. This starting point parallels with the approach of David Hume: all concepts and mental constructs must have direct correspondence with observations, either directly or indirectly.

Schyns, A modular neural network model of concept acquisition. Cognitive Science, 15: 461–508, 1991. 20 Artificial Intelligence 62. J. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World. New York: Irwin/McGraw-Hill, 2000. 63. C. ), Encyclopedia of Artificial Intelligence, Vols. 1–2. New York: John Wiley & Sons, 1987. 64. G. Shafer andJ. ), Uncertain Reasoning. Los Altos, California: Morgan Kaufmann Publishers, 1990. 65. A. Simon, The Sciences of the Artificial, 3rd ed. Cambridge, Massachusetts: MIT Press, 1996.

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