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Stimulating student engagement with an AI board game tournament

Ken Hasselmann, Quentin Lurkin

TL;DR

The paper presents a hands-on course to stimulate student engagement with AI by pairing project-based learning with a board game tournament. It details course design, including audience, topics, project setup, a tournament server, AI agents, evaluation, and gamification components, and discusses how competition is balanced with collaborative learning. Early informal results suggest strong motivation, high attendance, and that many groups successfully implemented capable AI agents, with top teams applying advanced adversarial search techniques such as iterative deepening minimax with alpha-beta pruning and transposition tables. The approach demonstrates a practical path for teaching AI fundamentals and software engineering through embodied competition, and provides a publicly available tournament system for replication.

Abstract

Strong foundations in basic AI techniques are key to understanding more advanced concepts. We believe that introducing AI techniques, such as search methods, early in higher education helps create a deeper understanding of the concepts seen later in more advanced AI and algorithms courses. We present a project-based and competition-based bachelor course that gives second-year students an introduction to search methods applied to board games. In groups of two, students have to use network programming and AI methods to build an AI agent to compete in a board game tournament-othello was this year's game. Students are evaluated based on the quality of their projects and on their performance during the final tournament. We believe that the introduction of gamification, in the form of competition-based learning, allows for a better learning experience for the students.

Stimulating student engagement with an AI board game tournament

TL;DR

The paper presents a hands-on course to stimulate student engagement with AI by pairing project-based learning with a board game tournament. It details course design, including audience, topics, project setup, a tournament server, AI agents, evaluation, and gamification components, and discusses how competition is balanced with collaborative learning. Early informal results suggest strong motivation, high attendance, and that many groups successfully implemented capable AI agents, with top teams applying advanced adversarial search techniques such as iterative deepening minimax with alpha-beta pruning and transposition tables. The approach demonstrates a practical path for teaching AI fundamentals and software engineering through embodied competition, and provides a publicly available tournament system for replication.

Abstract

Strong foundations in basic AI techniques are key to understanding more advanced concepts. We believe that introducing AI techniques, such as search methods, early in higher education helps create a deeper understanding of the concepts seen later in more advanced AI and algorithms courses. We present a project-based and competition-based bachelor course that gives second-year students an introduction to search methods applied to board games. In groups of two, students have to use network programming and AI methods to build an AI agent to compete in a board game tournament-othello was this year's game. Students are evaluated based on the quality of their projects and on their performance during the final tournament. We believe that the introduction of gamification, in the form of competition-based learning, allows for a better learning experience for the students.
Paper Structure (11 sections, 2 figures)

This paper contains 11 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Course design
  3. Audience
  4. Lecture topics
  5. Project
  6. Tournament system
  7. AI agents
  8. Evaluation
  9. Gamification components
  10. Course assessment
  11. Conclusion

Figures (2)

  • Figure 1: The board of othello. Othello is a two-player strategy game (a fixed initial setup variant of reversi), where players take turns placing disks of their assigned color on the board. Disks have one black and one white side. When placing a disk, any of the opponent's disks that are in line and bounded by disks of the current player are turned over to the current player's color. Once the board is full, at the end of the game, the player whose color is assigned to a majority of disks wins the game.
  • Figure 2: GUI of the tournament software. The current game being played is displayed on the top right; below, is the list of queued games. The left column shows all connected clients (AI agents).