Developing intelligent agents for training systems that learn their strategies from expert players

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Title: Developing intelligent agents for training systems that learn their strategies from expert players
Author: Whetzel, Jonathan Hunt
Abstract: 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 .
URI: http : / /hdl .handle .net /1969 .1 /2662
Date: 2005-11-01

Citation

Developing intelligent agents for training systems that learn their strategies from expert players. Available electronically from http : / /hdl .handle .net /1969 .1 /2662 .

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