Faculty Advisor or Committee Member

Charles Rich, Advisor

Faculty Advisor or Committee Member

Joseph E. Beck, Advisor

Faculty Advisor or Committee Member

David Finkel, Reader

Faculty Advisor or Committee Member

Michael A. Gennert, Department Head

Identifier

etd-042610-090201

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.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Computer Science

Project Type

Thesis

Date Accepted

2010-04-26

Accessibility

Unrestricted

Subjects

Causal, Model, Video Game, Causal Mode, Machine Learning

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