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Graph Algorithms for AI in Games [Video]

More Information
  • Make graphs to represent your game state
  • Use the breadth first search on your regular graphs
  • Implement the depth first search with your usual graphs
  • Use pathfinding in your grid and mazes
  • Work with optimizing the Heuristics in your game
  • Implement A* Search for a more balanced Heuristics
  • Create your very own Pac Mac like Game

Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect a set of computers into a network you will need algorithms on graphs.

For using the efficient algorithm to automatically find communities and opinion leaders on Facebook, you're going to work with graphs and algorithms on graphs. This course will serve as an introduction to graphs and present their increasingly complex algorithms that work on graphs. In the course, you will start by understanding how graphs can be used in games to represent various states and how searching graphs can help us. The course will introduce you to pathfinding, which is one of the most commonly solved problems in game AI. The course will then show you how to Optimize the pathfinding.

Finally, at the end of the course, you will learn the concept of meta-heuristics which can be used to find general solutions in complex domains.

Style and Approach

The video is packed with step-by-step instructions, working examples, and helpful advice. You will learn about Graph Algorithms for AI in Games. This practical course is divided into clear byte size chunks so you can learn at your own pace and focus on the areas of most interest to you.

  • Solve the most common graphs problems with increasing complexity of the algorithms.
  • Implement learning algorithms that work on graphs and concepts to build your own games in AI
  • A comprehensive course to gain better understanding of the fundamental concepts and graph algorithms used in games
Course Length 2 hours 41 minutes
ISBN 9781788472180
Date Of Publication 6 Dec 2017


Daniel Jallov

Daniel Jallov loves developing games with a flair for artificial intelligence and procedural content generation. He wrote a Master’s Thesis on how to procedurally generate artificial intelligence and how to build a game where the uncertainties of procedurally generated AI are used to engage the users rather than scare them away.
He is by no means a competitive gamer, but he enjoys games as a way to relax, to get challenged and to be entertained. I love the cinematic feel of modern AAA games, and he loves the intense game play experience of Super Hexagon and Hotline Miami. He loves getting lost in a deep story, and grinding for loot in a deep cave. He loves isolating himself in another world and playing local multiplayer’s with friends.