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12. Understanding Agent Behaviors in Game Environments

This video was recorded at AAAI 2008: AI Video Competition. Visual representation of logged game player data from both real and artificial agents can provide insight in order to discover patterns and behavior that would otherwise be hidden. We explore the use of visual data mining and automated data processing to investigate the logged player data and present the results of finding five unique phenomena in our dataset of 3079 players. These phenomena include pirouettes, flusters, jumpers, learning, and emergent behavior. Using the visual data mining process, we can find occurrences of such behavioral patterns in order to understand player behavior and improve the interactive experience.

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science / Intelligent Systems

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Material Type:: Presentation
Date Added to MERLOT:: February 8, 2015
Date Modified in MERLOT:: February 8, 2015
Authors:: Priyesh N. Dixit, University of North Carolina at Charlotte
G. Michael Youngblood, Game Intelligence Group, University of North Carolina at Charlotte
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Accessibility Information Available:: Unknown
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States