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Defeating bots with machine learning

Cheating software has become so advanced that it can get around existing detection methods. In this webinar we cover how ML-driven cheat detection is able to flag not only known, but also new unseen bots and cheats. We look into the different techniques of our machine learning approach and how we model human behavior and then compute the difference of new observations to this reference model. We discuss how much training data is required and how models are learning as your player base changes.

Game playing bots for game development

In this webinar we discuss using game playing bots, built on machine learning, for testing, balancing, or as player stand-ins and opponents. We open with a number of cases and patterns for using game playing bots based on machine learning. We look into the underlying technology and how to train from player data in order to simulate human behavior.

Optimize your game architecture for AI

How do you optimize your game design, architecture, and data collection process for future implementation of AI. In this webinar we talk about how to architect your game so it supports AI and player data collection from the ground up. Even if you are not planning on using machine learning for your game now, a few architectural decisions can make it easier to integrate down the line if you see the need.

Empowering the Biggest Games