4 min read

All around us, our perception of learning and intellect is being challenged daily with the advent of new and emerging technologies. From self-driving cars, playing Go and Chess, to computers being able to beat humans at classic Atari games, the advent of a group of technologies we colloquially call Machine Learning have come to dominate a new era in technological growth – a new era of growth that has been compared with the same importance as the discovery of electricity and has already been categorized as the next human technological age.

Games and simulations are no stranger to AI technologies and there are numerous assets available to the Unity developer in order to provide simulated machine intelligence. These technologies include content like Behavior Trees, Finite State Machine, navigation meshes, A*, and other heuristic ways game developers use to simulate intelligence. So, why Machine Learning and why now?

The reason is due in large part to the OpenAI initiative, an initiative that encourages research across academia and the industry to share ideas and research on AI and ML. This has resulted in an explosion of growth in new ideas, methods, and areas for research. This means for games and simulations that we no longer have to fake or simulate intelligence. Now, we can build agents that learn from their environment and even learn to beat their human builders.

This article is an excerpt taken from the book ‘Learn Unity ML-Agents – Fundamentals of Unity Machine Learning’  by Micheal Lanham. In this article, we look at the role that machine learning plays in game development.

Machine Learning is an implementation of Artificial Intelligence. It is a way for a computer to assimilate data or state and provide a learned solution or response. We often think of AI now as a broader term to reflect a “smart” system. A full game AI system, for instance, may incorporate ML tools combined with more classic AIs like Behavior Trees in order to simulate a richer, more unpredictable AI. We will use AI to describe a system and ML to describe the implementation.

How Machine Learning is useful in gaming

Game engines have embraced the idea of incorporating ML into all aspects of its product and not just for use as a game AI. While most developers may try to use ML for gaming, it certainly helps game development in the following areas:

  • Map/Level Generation: There are already plenty of examples where developers have used ML to auto-generate everything from dungeons to the realistic terrain. Getting this right can provide a game with endless replayability, but it can be some of the most challenging ML to develop.
  • Texture/Shader Generation: Another area that is getting the attention of ML is texture and shader generation. These technologies are getting a boost brought on by the attention of advanced generative adversarial networks, or GAN. There are plenty of great and fun examples of this tech in action; just do a search for DEEP FAKES in your favorite search engine.
  • Model Generation: There are a few projects coming to fruition in this area that could greatly simplify 3D object construction through enhanced scanning and/or auto-generation. Imagine being able to textually describe a simple model and having ML build it for you, in real-time, in a game or other AR/ VR/MR app, for example.
  • Audio Generation: Being able to generate audio sound effects or music on the fly is already being worked on for other areas, not just games. Yet, just imagine being able to have a custom designed soundtrack for your game developed by ML.
  • Artificial Players: This encompasses many uses from the gamer themselves using ML to play the game on their behalf to the developer using artificial players as enhanced test agents or as a way to engage players during low activity. If your game is simple enough, this could also be a way of auto testing levels.
  • NPCs or Game AI: Currently, there are better patterns out there to model basic behavioral intelligence in the form of Behavior Trees. While it’s unlikely that BTs or other similar patterns will go away any time soon, imagine being able to model an NPC that may actually do an unpredictable, but rather cool behavior. This opens all sorts of possibilities that excite not only developers but players as well.

So, we learned about different areas of the gaming world such as model generation, artificial players, NPCs, level generation, etc, where Machine learning can be extensively used.

If you found this post useful, be sure to check out the book ‘Learn Unity ML-Agents – Fundamentals of Unity Machine Learning’ to learn more machine learning concepts in gaming.

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