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Game Design Through Self-play Experiments by Alasdair Macleod is a scholarly article available to read on EtoBox.

What is Game Design Through Self-play Experiments about?

The application of self-play experiments to computer games was pioneered by Thompson in 1982 with his chess machine BELLE. Since then the technique has been widely used in a variety of games to train artificial players employing a range of artificial neural network architectures. Of particular note is the TD-learning Backgammon program of Tesauro developed in 1995. When developing artificial game players that learn by experience, it is generally possible to accelerate the training process through selfplay. Compared with training by humans, this confers the advantages of greater speed and a precise control of playing strength through parameter variation. In spite of these potential advantages, the use of self-play experiments is considered by many to be a treacherous road fraught with problems. The value of such experiments is unclear and the threshold of learning that can be achieved through self-play alone is unknown. There is the common-sense perception that only limited playing skill can be achieved through machine self-play, a notion that is challenged here. A new application that is immune from the problems associated with machine learning is the use of self-play experiments t

Author

Alasdair Macleod

Publisher

ACM

Published

2005

Language

EN