Etd

 

Applying Causal Models to Dynamic Difficulty Adjustment in Video Games Public

Downloadable Content

open in viewer

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.

Creator
Contributors
Degree
Unit
Publisher
Language
  • English
Identifier
  • etd-042610-090201
Keyword
Advisor
Defense date
Year
  • 2010
Date created
  • 2010-04-26
Resource type
Rights statement
License

Relationships

In Collection:

Items

Permanent link to this page: https://digital.wpi.edu/show/cj82k740x