When creating a video game, every digital character must be created by professional artists. Their work is very labor intensive because the number of created characters are in the thousands, each of which has multiple visual components that must be created for each one. “Scanning” real actors to create a digital version of themselves can help speed up this process, but each scan must be altered to preserve the actor’s anonymity.
Ubisoft’s cloud-based video game ecosystems experience the workload up to 5+ millions players in a typical week. Workloads on game servers are of different scales, ranging from tens of clients per game server to thousands of clients for traditional workloads. To guarantee game player user experience, a pool of servers is launched to react to demands but servers are idles in most of the time. Scaling down servers is even more complex because of the persistent connections to maintain the states and records of players and games.
Modern software organizations use continuous integration (CI) practices to build and test their products after each code change in order to detect quality issues as soon as possible. Unfortunately, the number of builds scales super-linearly with the number of hardware and feature configurations that should be supported. In order to avoid running out of build resources, organizations are no longer able to build individual code changes, but instead need to build groups of successive code changes. Worse, certain ?flaky?
The goal of this project is to explore the use of log analytics and machine/deep learning techniques to improve Ubisoft operational intelligence. Logs contain a wealth of information, but often hindered by the lack of best practices, tools, and processes. Despite the importance of logging, the area has not evolved much over the years. At Ubisoft, logs are used extensively for various system diagnosis tasks. The analysis of logs, however, is usually performed manually, limiting the full potential of the information contained in logs.
Making robots walk and balance as well as humans is extremely difficult. New techniques involving machine learning have shown promise in getting robots to mimic the movements of humans recorded using motion capture
technology widely used for videogames and movies. While these techniques show promise, they are still in development, and have difficulty switching between behaviours. Its still very difficult for robots to go from standing still to running. They also fall over very easily when pushed or tripped, since they don't have a concept of reacting to pushes in the same way that people do.
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