Using AI to play games is often perceived as an early step towards 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 and actions rules are often well-defined and abstract. People have been interested in using AI to play games for quite a while. Recent development of deep neural networks allowed visual information in games to be processed effectively and directly used for decision making of agents. And the area of deep reinforcement learning and meta-learning are also being explored in this aspect.
In this presentation we will share experiences from our attempts in using AI on Spark for game playing. The talk will include demos and some details of the experiments, and our learnings, for example, whether Spark is a good fit for implementing game related AI, which parts needs to be improved, and the changes of Spark in the area of AI game playing.
Shengsheng (Shane) Huang is a software architect at Intel and an Apache Spark committer and PMC member, leading the development of large-scale analytical applications and infrastructure on Spark in Intel. Her area of focus is big data and distributed machine learning, especially deep (convolutional) neural networks. Previously at NUS (National University of Singapore), her research interests are large-scale vision data analysis and statistical machine learning.
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