Towards Learned Anticipation in Complex Stochastic Environments
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Authors
Darken, Christian J.
Subjects
Advisors
Date of Issue
2005
Date
2005
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Abstract
We describe a novel methodology by which a software agent can learn to predict future events in complex stochastic environmentals. It is particularly relevant to environments that are construed specifically so as to be able to support high-performance software agents, such as video games. We present results gathered from a first prototype of our approach. The technique presented may have applications that range beyond improving agent performance, in particular to user modeling in the service of automated game testing.
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Description
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Department
Computer Science (CS)
Organization
Naval Postgraduate School (U.S.)
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Format
Citation
Proceedings of AIIDE 2005.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.