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Simulating Strategy and Dexterity for Puzzle Games
IEEE Conference on Computational Intelligence and Games, August 2017

Aaron Isaksen, Drew Wallace, Adam Finkelstein,
Andy Nealen


Examples from Tetris demonstrating how players use strategy to decide between multiple moves and use dexterity to correctly execute such moves.

Abstract

We examine the impact of strategy and dexterity on video games in which a player must use strategy to decide between multiple moves and must use dexterity to correctly execute those moves. We run simulation experiments on variants of two popular, interactive puzzle games: Tetris, which exhibits dexterity in the form of speed-accuracy time pressure, and Puzzle Bobble, which requires precise aiming. By modeling dexterity and strategy as separate components, we quantify the effect of each type of difficulty using normalized mean score and artificial intelligence agents that make human-like errors. We show how these techniques can model and visualize dexterity and strategy requirements as well as the effect of scoring systems on expressive range.

Citation (BibTeX)

Aaron Isaksen, Drew Wallace, Adam Finkelstein, and Andy Nealen. Simulating Strategy and Dexterity for Puzzle Games. IEEE Conference on Computational Intelligence and Games, August 2017.

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