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dc.contributor.authorDarken, Christian J.
dc.contributor.otherMOVES Institute
dc.date2005
dc.date.accessioned2013-09-25T22:58:00Z
dc.date.available2013-09-25T22:58:00Z
dc.date.issued2005
dc.identifier.citationProceedings of AIIDE 2005.
dc.identifier.urihttps://hdl.handle.net/10945/36613
dc.description.abstractWe 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.en_US
dc.rightsThis 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.en_US
dc.titleTowards Learned Anticipation in Complex Stochastic Environmentsen_US
dc.contributor.corporateNaval Postgraduate School, Monterey, California
dc.contributor.departmentComputer Science (CS)


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