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Simulating strategy and dexterity for puzzle games

Author(s): Isaksen, Aaron; Wallace, Drew; Finkelstein, Adam; Nealen, Andy

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dc.contributor.authorIsaksen, Aaron-
dc.contributor.authorWallace, Drew-
dc.contributor.authorFinkelstein, Adam-
dc.contributor.authorNealen, Andy-
dc.identifier.citationIsaksen, Aaron, Drew Wallace, Adam Finkelstein, and Andy Nealen. "Simulating strategy and dexterity for puzzle games." IEEE Conference on Computational Intelligence and Games (CIG) (2017): pp. 142-149. doi:10.1109/CIG.2017.8080427en_US
dc.description.abstractWe 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.en_US
dc.format.extent142 - 149en_US
dc.relation.ispartofIEEE Conference on Computational Intelligence and Games (CIG)en_US
dc.rightsAuthor's manuscripten_US
dc.titleSimulating strategy and dexterity for puzzle gamesen_US
dc.typeConference Articleen_US

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