Game design through self-play experiments

Abstract

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 to test the integrity and fairness of games and modify the rules accordingly. We will show how the rules of a particular game, Perudo, can be analysed for fairness and how the excessive positive feedback that arises when forces become unbalanced can be curbed. We use the notion of fair in the same sense as in a soccer game -if a team loses a goal, neglecting psychological effects, the chance of losing a second goal is not significantly changed. It is recognised that the cumulative growth in advantage is part of many games and that it is inappropriate to alter the rules in these cases. However the rate at which advantages grow can be moderated by rule alterations. We will also consider the application of the technique to a range of traditional games. In chess, for example, White is considered to have an advantage over Black. The imbalance can be determined for different playing strengths and extrapolated. We will show that the principles can be extended to the more complex situations of computer games and propose that the development of unintelligent agents to explore game play is advantageous.

A Framework for the Semi-Automatic Testing of Video Games

“…Also research in AI has investigated human entertainment models for measuring qualitative factors such as challenge and curiosity (Yannakakis & Hallam, 2006) and approaches for analyzing and reasoning about the user experience when interacting with virtual environments (Yang, Marsh, & Shahabi, 2005). Recently self-playing experiments have been applied to computer games with the aim of modeling the integrity and fairness in rudimentary computer games, assisting the game design (Macleod, 2005). Denzinger et al (2004) have applied similar systems to a complex game (FIFA-99) showing how their agents, employing a genetic algorithm (GA), were able to find faults in the game mechanics.…”

Scaling the Level of Difficulty in Single Player Video Games

“…Alasdair Macleod studied gameplay testing of Perudo, a bidding dice game, simulating plays with a multi-agent system [13]. He wanted to modify Perudo's gameplay to make it more fair, and added a rule he thought it would help loosing players to stay in the game.…”