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Description:
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Computer -based training systems have become a mainstay in military and
private institutions for training people how to perform certain complex tasks . As
these tasks expand in difficulty , intelligent agents will appear as virtual teammates
or tutors assisting a trainee in performing and learning the task . For developing
these agents , we must obtain the strategies from expert players and emulate their
behavior within the agent . Past researchers have shown the challenges in acquiring
this information from expert human players and translating it into the agent . A
solution for this problem involves using computer systems that assist in the human
expert knowledge elicitation process . In this thesis , we present an approach for
developing an agent for the game Revised Space Fortress , a game representative of
the complex tasks found in training systems . Using machine learning techniques ,
the agent learns the strategy for the game by observing how a human expert plays .
We highlight the challenges encountered while designing and training the agent in
this real -time game environment , and our solutions toward handling these
problems . Afterward , we discuss our experiment that examines whether trainees
experience a difference in performance when training with a human or virtual
partner , and how expert agents that express distinctive behaviors affect the
learning of a human trainee . We show from our results that a partner agent that
learns its strategy from an expert player serves the same benefit as a training
partner compared to a programmed expert -level agent and a human partner of
equal intelligence to the trainee . |