Game playing using AI on Spark

This will be presented in English.

16:2017:00 Thursday, June 20, 2019

必要预备知识 (Prerequisite Knowledge)

  • General knowledge of Spark essentials and deep neural networks basics
  • Familiarity with reinforcement learning (useful but not required)

您将学到什么 (What you'll learn)

  • Discover the pros and cons of using Spark for implementing game-related AI

描述 (Description)

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 meta-learning are also being explored in this aspect.

Shengsheng Huang takes you through her experiences from her attempts in using the AI on Spark for playing games. She provides demos and some details of the experiments and what she learned, 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.



Photo of Shengsheng Huang

Shengsheng Huang


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 the National University of Singapore (NUS), her research interests are large-scale vision data analysis and statistical machine learning.

Shengsheng(Shane)Huang是英特尔的软件架构师,也是Apache Spark的贡献者和PMC成员。她领导着英特尔基于Spark的大规模分析应用和基础架构的开发。她关注的领域是大数据和分布式机器学习,尤其是深度(卷积)神经网络。她之前就读于新加坡国立大学(NUS),研究兴趣是大规模视觉数据分析和统计机器学习。

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