Dissertations and Theses @ UNI
Availability
Open Access Thesis
Keywords
Algorithms; Machine learning;
Abstract
This study compares the effectiveness of inductive and analytical learning techniques for a specific type of problem. The set of problems has a large number of parameter combinations as inputs, and a small set of possible output solutions. The card game, Euchre, was the basis for research. Four algorithms were used for comparison. Two were variants of induction using decision tree learning, and the other two used variants of explanation-based learning, an analytical approach. The example data were gathered from human players, along with one set of random, "noisy" data. These data were then used as training sets for the four learners. Each of the training sets contained 90 examples, with an additional 30 used for testing the learning system. In addition to self-testing, each of the four systems was pitted against each other in simulated games to see which was the most effective in playing. The results demonstrate that the analytical approaches benefit greatly from knowing the domain rules previous to learning. This enabled the learner to focus only on learning the strategy behind each play. The inductive approaches proved unable to learn these domain rules. They were unable to identify the difference between a game rule and a strategic one. Therefore, the rules induced were a meld of the two, and the rules generated caused illegal choices, resulting in losing hands. The inductive learners were able to overcome this limitation by being forced to follow the rules. When not allowed to play an illegal card, the inductive learners performed comparably to the analytic ones. This indicates that the overhead, and increased processing required by the analytical approaches, offers no benefit in problems of this type.
Year of Submission
2001
Degree Name
Master of Science
Department
Department of Computer Science
First Advisor
Eugene Wallingford
Second Advisor
Mark Fienup
Third Advisor
Jack Yates
Date Original
2001
Object Description
1 PDF file (42 leaves)
Copyright
©2001 Michael J. Holmes
Language
en
File Format
application/pdf
Recommended Citation
Holmes, Michael J., "Machine Learning in Euchre: A Comparison of Techniques" (2001). Dissertations and Theses @ UNI. 2957.
https://scholarworks.uni.edu/etd/2957
Comments
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