For a while now, I wanted to introduce you to a project that I started around two years ago. While not complete, the project has reached enough maturity so I can talk about it, show nice screen shots and envision its release.
This project is called , and it is a video game in the mindset of Build and Defend. Essentially, Dust contains many of the things that I wanted to add to B&D, but that I could not because it would have completely changed the game, or because it would have required to completely reprogram the game engine. Dust is a top-down, third person 2D game in a 3D world, where players try to survive in an post apocalyptic world filled with strange creatures as simple as that. The game is played real time (i.e. it is not turn based) in an gigantic and procedurally generated world populated with different areas and encounters. I would say that Dust is a Rogue Like (if you do not follow the very strict definition).
Continue reading “Dust Presentation discussion about Artificial Intelligence engine”
In the last two posts (Machine Learning “for Dummies” [part 1] and Machine Learning “for Dummies” [part 2]) we introduced what was the “Classification” of Machine Learning. We also presented a very famous algorithm called “k-nearest neighbors”. If you have not read these posts, you may want to do it now to be sure to understand this post.
Continue reading “Machine Learning “for Dummies” (part 3) : The Decision tree and Decision Forest algorithms”
In the last post, we introduced what was the « Classification » of Machine Learning. We also presented some examples of applications (banks, hospitals, etc.) and we started to play with the Iris dataset. If you have not read the first post, now would be a good time in order to fully understand the following article.
Back to the Iris story: Remember, we have a list of iris flowers described by the length and width of their petals and sepals (the attributes). Also, we know the species of all except one iris (we will call this iris the “mysterious iris”), and we would like to find the species (or class) of the mysterious iris. For this task, we suppose the attributes of the irises to be indicative of the species.
Continue reading “Machine Learning “for Dummies” (part 2) : The K-nearest neighbors algorithm”
You might have already heard about the terms “Machine Learning” (or “Data Mining”, or “Big data”, or Data Analysis, or Data Science, or Cloud Computing) but you never actually knew what they meant. Maybe you know those are computer or mathematical stuffs and that is all. Maybe you have to use some softwares that relies on Machine Learning in your job, but for you, it is like magic.
Continue reading “Machine Learning for Dummies (part 1) : Introduction to Classification”