Worcester Polytechnic Institute Electronic Theses and Dissertations Collection

Title page for ETD etd-042610-090201


Document Typethesis
Author NameMoffett, Jeffrey P
URNetd-042610-090201
TitleApplying Causal Models to Dynamic Difficulty Adjustment in Video Games
DegreeMS
DepartmentComputer Science
Advisors
  • Charles Rich, Advisor
  • Joseph Beck, Advisor
  • David Finkel, Reader
  • Michael Gennert, Department Head
  • Keywords
  • Causal
  • Model
  • Video Game
  • Causal Mode
  • Machine Learning
  • Date of Presentation/Defense2010-04-22
    Availability unrestricted

    Abstract

    We have developed a causal model of how various aspects of a computer game influence how much a player enjoys the experience, as well as how long the player will play. This model is organized into three layers: a generic layer that applies to any game, a refinement layer for a particular game genre, and an instantiation layer for a specific game. Two experiments using different games were performed to validate the model. The model was used to design and implement a system and API for Dynamic Difficulty Adjustment(DDA). This DDA system and API uses machine learning techniques to make changes to a game in real time in the hopes of improving the experience of the user and making them play longer. A final experiment is presented that shows the effectiveness of the designed system.

    Files
  • JeffMoffett_Thesis.pdf

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