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RubyConf 2018 - Beating Mastermind: Winning with the help of Donald Knuth by Adam Forsyth Come solve logic games with Ruby, learn about minimax algorithms, and turn a paper into code. There are lots of "solved" problems out there that don't have a good library available -- learn how to write one yourself. We'll take a paper written by Donald Knuth (the "father of analysis of algorithms" and author of "The Art of Computer Science") and use it to solve a game called Mastermind that many people (including me) played as children. We'll look at the game, walk through the paper, turn the prose and math into code, understand minimax algorithms, and never lose at Mastermind again!
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In the talk titled "Beating Mastermind: Winning with the help of Donald Knuth," Adam Forsyth presents a detailed exploration of the board game Mastermind through the lens of algorithm design, specifically focusing on the minimax algorithm. The session, part of RubyConf 2018, aims to help programmers convert theoretical algorithmic concepts from academic papers into practical code implementations. Forsyth begins with an overview of Mastermind, a code-breaking game with a rich history, where players attempt to guess a hidden code based on feedback. He demonstrates gameplay mechanics, illustrating how players progressively narrow down possibilities through logical deductions and strategic guessing. Key Points Discussed: - **Introduction to Mastermind**: Forsyth explains the rules of the game, where two players engage in code-making and code-breaking, providing visual examples of possible guesses and scores received. - **Historical Reference**: He references a foundational paper by Donald Knuth from 1976, which asserts that a player can guarantee a win within five moves using optimized strategies. - **Minimax Algorithm Explanation**: Forsyth elaborates on the minimax algorithm, describing its application for making optimal decisions in game theory, particularly in scenarios like Mastermind where there are competing objectives. - **Algorithm Implementation**: The talk includes a walkthrough of implementing the minimax algorithm, which assesses potential guesses and their outcomes to minimize the maximum number of remaining possibilities. - **Interactive Demonstration**: Forsyth shows a Ruby implementation of Mastermind that utilizes the minimax algorithm to automate gameplay, demonstrating how it adapts based on previous guesses and scores. Significant Examples: - Forsyth highlights specific example games to emphasize the iterative guessing process, where players must balance logical deductions with strategic risk-taking. He illustrates how an impossible guess can sometimes yield beneficial results in eliminating potential scores, thus leading to victory. Conclusions and Takeaways: - The central message is the importance of understanding the underlying algorithmic principles in effectively solving problems encountered in games like Mastermind. - Forsyth encourages programmers to take academic concepts seriously by translating them into practical applications, thereby gaining confidence in both coding and algorithmic thinking. - The session concludes with Forsyth inviting the audience to explore the source code available on GitHub post-talk, reinforcing the practice of sharing knowledge and fostering community learning among developers.
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