Towards Learned Anticipation in Complex Stochastic Environments

dc.contributor.author	Darken, Christian J.
dc.contributor.corporate	Naval Postgraduate School (U.S.)
dc.contributor.department	Computer Science (CS)
dc.contributor.other	MOVES Institute
dc.date	2005
dc.date.accessioned	2013-09-25T22:58:00Z
dc.date.available	2013-09-25T22:58:00Z
dc.date.issued	2005
dc.description.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.	en_US
dc.identifier.citation	Proceedings of AIIDE 2005.
dc.identifier.uri	https://hdl.handle.net/10945/36613
dc.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.	en_US
dc.title	Towards Learned Anticipation in Complex Stochastic Environments	en_US
dspace.entity.type	Publication

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