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LEARNING HUMAN BEHAVIOR FROM OBSERVATION FOR GAMING APPLICATIONS

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Date Issued:
2007
Abstract/Description:
The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. One focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are being used scarcely in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment to create game agents (bots) in. This work attempts to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces Quake 2 agents that behave as a human player trains them to act. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO is a technique that offers the gaming community a mode of game play that has promising entertainment value.
Title: LEARNING HUMAN BEHAVIOR FROM OBSERVATION FOR GAMING APPLICATIONS.
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Name(s): Moriarty, Christopher, Author
Gonzalez, Avelino, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2007
Publisher: University of Central Florida
Language(s): English
Abstract/Description: The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. One focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are being used scarcely in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment to create game agents (bots) in. This work attempts to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces Quake 2 agents that behave as a human player trains them to act. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO is a technique that offers the gaming community a mode of game play that has promising entertainment value.
Identifier: CFE0001694 (IID), ucf:47201 (fedora)
Note(s): 2007-05-01
M.S.Cp.E.
Engineering and Computer Science, School of Electrical Engineering and Computer Science
Masters
This record was generated from author submitted information.
Subject(s): Artificial Intelligence
Learning from observation
Neural Networks
Quake
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0001694
Restrictions on Access: public
Host Institution: UCF

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