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A Computerized Functional Skills Assessment and Training Program Targeting Technology Based Everyday Functional Skills
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A Monte Carlo Approach to Skill-Based Automated Playtesting.

Britton Horn1, Josh Aaron Miller1, Gillian Smith2

  • 1Northeastern University, Boston, Massachusetts, USA.

Proceedings. AAAI Artificial Intelligence and Interactive Digital Entertainment Conference
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Automated player simulations called Stratabots were applied to the game Foldit to analyze learning progressions. Stratabot analysis revealed mostly smooth learning in tutorials, indicating potential for automated playtesting in game design.

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Area of Science:

  • Educational Technology
  • Game Design
  • Human-Computer Interaction

Background:

  • Designing effective learning progressions in games is challenging due to the dynamic nature of content.
  • Analyzing the quality of these progressions during the design phase requires robust methodologies.

Purpose of the Study:

  • To apply automated player simulations (Stratabots) to analyze learning progressions in the human computation game Foldit.
  • To evaluate the effectiveness of Stratabots in assessing game-based learning design.

Main Methods:

  • Stratabots, which are automated player simulations modeling diverse player skills, were implemented in Foldit.
  • Stratabot performance was analyzed in conjunction with actual player data.
  • General gameplaying algorithms like Monte Carlo Evaluation were leveraged.

Main Results:

  • Stratabot analysis indicated a largely smooth learning progression within the tutorial levels of Foldit.
  • Evidence for areas of improvement in the learning progression was identified.
  • The use of general gameplaying algorithms reduced development time for automated playtesting.

Conclusions:

  • Stratabots offer a viable method for analyzing learning progressions in games, even with evolving content.
  • Automated playtesting using Stratabots can provide valuable insights for game designers.
  • Leveraging existing algorithms enhances the efficiency of automated playtesting without compromising predictive accuracy.