Thesis available on neuroprose

[Mike Gherrity]

FTP-host: archive.cis.ohio-state.edu
FTP-filename: /pub/neuroprose/gherrity.thesis.ps.Z

A postscript copy of my PhD thesis has been placed in the neuroprose archive.
It prints on 110 pages. The abstract is given below, followed by retrieval
instructions.

 Mike Gherrity
 e-mail: gherrity at nosc.mil

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                           A Game-Learning Machine

                                  ABSTRACT

This disertation describes a program which learns good strategies for
two-person, deterministic, zero-sum board games of perfect information.
The program learns by simply playing the game against either a human or
computer opponent.  The results of the program's learning the games of
tic-tac-toe, connect-four, and chess are reported.

The program consists of a game-independent kernel and a game-specific
move generator module.  Only the move generator is modified to reflect
the rules of the game to be played.  The kernel remains unchanged for
different games.  The kernal uses a temporal difference procedure
combined with a backpropagation neural network to learn good evaluation
functions for the game being played.

Central to the performance of the program is the consistency search
procedure.  This is a game-independent generalization of the capture tree
search used in most successful chess playing programs.  It is based on the
idea of using search to correct errors in evaluations of positions.
This procedure is described, analyzed, tested, and implemented in the
game-learning program.  Both the test results and the performance of the
program confirm the results of the analysis which indicate that
consistency search improves game playing performance for sufficiently
accurate evaluation functions.

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To retrieve from neuroprose:

unix> ftp cheops.cis.ohio-state.edu
Name (cheops.cis.ohio-state.edu:zemel): anonymous
Password: (use your email address)
ftp> cd pub/neuroprose
ftp> get gherrity.thesis.ps.Z
ftp> quit
unix> uncompress gherrity.thesis.ps.Z
unix> lpr gherrity.thesis.ps