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The Artificial Intelligence of ‘Guess It’

The Artificial Intelligence of ‘Guess It’. By: Will Lounsbury Monarch High School Mentor: Dr. Aaron Bradley CU Boulder. Purpose. To learn how effective Opponent Modeling and Hill Climbing Algorithms are at optimizing strategies in simple card games. Project Background.

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The Artificial Intelligence of ‘Guess It’

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  1. The Artificial Intelligence of ‘Guess It’ By: Will Lounsbury Monarch High School Mentor: Dr. Aaron Bradley CU Boulder

  2. Purpose To learn how effective Opponent Modeling and Hill Climbing Algorithms are at optimizing strategies in simple card games.

  3. Project Background • Interest in Computer Science from Robotics • Be at a level that allows autonomy • Card games • Do something with A.I. • Considered poker, too complex

  4. Guess It • Created as simple substitute for Poker • 15 card deck, 7 cards to each player • Must guess the remaining card • Can “ask” or “call” • Can “bluff” and “double bluff”

  5. Opponent Modeling • Collects data on opponent • Compares probabilities to make decisions • In Guess It: • Collects data on bluffing frequency • Probability of correctly guessing

  6. Hill Climbing • Optimization: • Finds maximums in the function • Guess and check • Problems: • Plateaus • Ridges • Local Maxima

  7. Method • Create a program with 2 “agents” • 3 “modes” of playing: • Dumb – ignores opponent bluffing • Smart – Uses Opponent Modeling • Super – Uses HC generated algorithm • Collect data on games between modes.

  8. Learning Curve for Opponent Modeling Jack Win Frequency Number of Games

  9. Smart vs. Smart Jack Win Percentage

  10. Smart win percentage versus smart and dumb opponents bluffing 30% Jack Win Percentage Jack Bluff percentage

  11. Smart vs. Smart Optimization Jack Ideal Bluff Percentage Opponent Bluff percentage

  12. Preliminary Super Agent vs. Others with varying bluff percentage Jack Win Percentage Dumb Smart Opponent Bluff Percentage

  13. Smart vs. Dumb optimization Ideal bluffing percentage Opponent Bluffing Frequency

  14. The Future • Finish the Super Agent • Fuzziness in the data • Find better curve (piecewise) • Changing bluffing frequency game to game • Meta-strategies • Double Bluffing • Random calling

  15. Conclusion • Bluffing is an extraordinarily integral strategic aspect of Guess It. • Both Opponent modeling and Hill climbing are highly effective at optimizing strategy in Guess It, especially when combined. • Knowing if the opponent is smart or dumb is crucial.

  16. Thanks to Dr. Aaron Bradley for being my mentor this past year, teaching me so much, and helping me to expand my programming horizons.

  17. Bibliography • Epstein, Richard A. The Theory of Gambling and Statistical Logic. New York: Academic, 1977. • Di Pierto, Anthony, Luigi Barone, and Lyndon While. "A Comparison of Different Adaptive Learning Techniques for Opponent Modelling in the Game of Guess It." Google Docs. Web. <http://docs.google.com/gview>. • Cesa-Bianchi, Nicolò, and Gabor Lugosi. Prediction, learning, and games. Cambridge: Cambridge UP, 2006. Print. • Packel, Edward. Mathematics of games and gambling. Mathematical Association of America, 1981. Print. • Isaacs, Rufus. "A Card Game With Bluffing." The American Mathematical Monthly 62.2: 99-108.

  18. THE END

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