This assignment allows students to gain experience with AI gameplaying algorithms, implementing minimax and alpha-beta pruning and designing a utility function for measuring game states. The assignment uses Connect Four, a relatively simple fully-observable and deterministic game that students are likely to have seen before. Students are responsible only for developing an agent to play the game; the game itself is already implemented and given as part of the student-facing materials. The assignment breaks down the requirements for the two algorithms into smaller chunks in order to make the whole assignment more approachable. We also provide code for Tic-Tac-Toe so that students can apply their code for minimax and alpha-beta pruning to a simpler game where suboptimal moves will be more obvious, indicating potential bugs in their implementation. The assignment allows for a tournament to be played among all student submissions, potentially awarding extra credit to the winner of the class tournament.
Educational-resources Downloads
This zip contains the supplemental materials for this article.
- Stuart J. Russell and Peter Norvig. 2009. Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall.Google Scholar
Digital Library
- Eric W. Weisstein. "Connect-Four." From MathWorld--A Wolfram Web Resource. Retrieved from https://mathworld.wolfram.com/Connect-Four.htmlGoogle Scholar
Index Terms
- AI: Connect Four Agent
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