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This paper provides a computational intelligence perspective on the design of intelligent video game agents. The paper explains why this is an interesting area to research, and outlines the most promising approaches to date, including evolution, temporal difference learning and Monte Carlo Tree Search. Strengths and weaknesses of each approach are identified, and some research directions are outlined that may soon lead to significantly improved video game agents with lower development costs.