Network tournament pedagogical approach involving game playing in artificial intelligence

Abstract

Game playing and genetic algorithms (GAs) are two important topics in artificial intelligence (AI). In this work we employ network tournament to assist in teaching these concepts associated with AI. Three exercises that implement a game-playing program are designed to help students learn relevant topics in AI. The first exercise involves game theory, e.g. mini-max search and alpha-beta pruning. The second exercise helps students understand the critical nature of a good heuristic function in game playing. And, the third exercise introduces GAs to learn a heuristic function. In addition to these exercises, this work also designs several programming toolkits to help students complete their exercises, such as a network tournament interface, a graphical man-machine interface, and a genetic algorithm based game-playing engine. These exercises encompass pertinent topics involving artificial intelligence and the network tournament. The network tournament provides a relatively easy means of Othello competition, and has merit in improving students' motivation for learning relevant topics.