Using AI to play games is often perceived as an early step toward achieving general machine intelligence, as the ability to reason and make decisions based on sensed information is an essential part of general intelligence. Games are good playgrounds for experimenting with intelligent agents as the goals, actions, and rules are often well-defined and abstract. People have been interested in using AI to play games for quite a while. The recent development of deep neural networks allowed visual information in games to be processed effectively and directly used for the decision making of agents, and the area of deep reinforcement learning and metalearning are also being explored in this aspect.
Shan Yu offers an overview of and shares lessons learned from her attempts using AI on Spark to play games: for example, whether Spark is a good fit for implementing game-related AI, which parts need to be improved, and the changes Spark has made in the area of AI game playing.
Shan Yu is a machine learning software engineer at Intel.
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