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Implementing AI to Play Games [Video]

More Information
  • Perform searches in games
  • Implement a game evaluation function in your game
  • Quantize the desirability of a move for your game
  • Explore a game tree using AI
  • Work on how to optimize a search
  • Design an evolutionary algorithm
  • Implement various stages of the evolutionary algorithm
  • Improve the performance of evolutionary algorithms by adding visualizations
  • How to solve a search which has certain constraints for the variables

In video games, Artificial Intelligence is used to generate responsive or intelligent behavior primarily in Non-Player Characters (NPCs), like human intelligence. In this course, we look at games; we understand how to decide which move to take based on future possibilities and payoffs (just as, in chess, we look n-moves ahead into the future).

We explore how to solve applications where there are a number of parameters to optimize, such as time or distance, and the possibilities are exponential. We look at how to design the various stage of the evolutionary algorithm that will control performance. We take a sample game—Tic-Tac-Toe—and show how various steps of the algorithm are implemented in code. And we look at color filling as a constraint satisfaction application and see how various algorithm concepts are applied in code.

Finally, we also explain a trip-planning application and see how the application is solved through evolutionary algorithms.

Style and Approach

A fun course packed with step-by-step instructions, working examples, and helpful advice.

You will learn how AI is used to make your games smarter. This comprehensive course is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to you.

  • Enter the world of games with AI
  • Comprehensive, fast, and friendly guide to implementing AI in your games and puzzles
  • Understand how to leverage different player and search strategies to make your algorithms smarter
Course Length 3 hours 23 minutes
Date Of Publication 31 Oct 2017


Devangini Patel

Devangini Patel is a PhD student at the National University of Singapore, Singapore. Her research interests include deep learning, computer vision, machine learning, and artificial intelligence. She has completed a master's in artificial intelligence at the University of Southampton, UK. She has over 5 years, experience in the field of AI and has worked on various industrial and research projects in AI, including facial expression analysis, robotics, virtual try-on, object recognition and detection, and advertisement ranking.