Experiences that seem real to players in training and exercise scenarios
enhance learning. Improving the fidelity of automated non-player
characters (NPCs) can increase the level of realism felt by players. In
this report, we describe how we used machine learning (ML) modeling to
create decision-making preferences for NPCs. In our research, we test ML
solutions and confirm that NPCs can exhibit lifelike computer activity
that improves over time.
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