Aaron Isaksen

Generating heuristics for novice players

We consider the problem of generating compact sub-optimal game-playing heuristics that can be understood and easily executed by novices. In particular, we seek to find heuristics that can lead to good play while at the same time be expressed as fast and frugal trees or short decision lists. This has applications in automatically generating tutorials and instructions for playing games, but also in analyzing game design and measuring game depth. We use the classic game Blackjack as a testbed, and compare condition induction with the RIPPER algorithm, exhaustive-greedy search in statement space, genetic programming and axis-aligned search. We find that all of these methods can find compact well-playing heuristics under the given constraints, with axis-aligned search performing particularly well.

Simulating strategy and dexterity for puzzle games

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.

Drawing without replacement as a game mechanic

We introduce several deck of cards and dice models that can be used to represent stochastic outcomes in tabletop games. We analyze these using a toy game introduced as a Micro Combat game. By simulating the outcome of the game with these different models we can analyze them in terms of their salience, disparity, fairness and obfuscation. We expect this analysis to help designers choose the method that best suits their intended experience.

Exploring Game Space of Minimal Action Games via Parameter Tuning and Survival Analysis

Game designers can use computer-aided game design methods to model how players may experience the perceived difficulty of a game. We present methods to generate and analyze the difficulty of a wide variety of minimal action game variants throughout game space, where each point in this abstract design space represents a unique game variant. Focusing on a parameterized version of Flappy Bird, we predict hazard rates and difficulty curves using automatic playtesting, Monte Carlo simulation, a player model based on human motor skills (precision and actions per second), and survival analysis of score histograms. We demonstrate our techniques using simulated game play and actual game data from over 106 million play sessions of a popular online Flappy Bird variant, showing quantitative reasons why balancing a game for a wide range of player skill can be difficult. Some applications of our techniques include searching for a specific difficulty, game space visualization, computational creativity to find unique variants, and tuning game balance to adjust the difficulty curve even when game parameters are time varying, score dependent, or changing based on game progress.