Yes, Cameron's work was one of our sources of inspiration. The Ludi system that produced Yavalath is clearly a milestone in research on automatic game design. Another source of inspiration was my own work on automatic game design for simple PacMan-like games, which was carried out at the same time as Cameron's work and is described in this paper:
http://julian.togelius.com/Togelius2008An.pdf
In general, this line of research is still in its infancy, as we are trying to figure out new ways of evaluating game quality and representing various aspects of games.

The original papers describing the work can be found here:
http://julian.togelius.com/Font2013Towards.pdf
and
http://julian.togelius.com/Font2013A.pdf

Similar evolutionary techniques have been used to generate a number of different types of game content, including Starcraft maps, Super Mario levels, rocks, dungeons, weapons... Here's an overview:
http://julian.togelius.com/Togelius2011Searchbased.pdf

It's not "blind" search like Prolog, or even depth-first search. It's objective driven search using artificial evolution. Actually, almost all successful AI uses search in a prominent role.

The aim of the competition is not merely to create random levels, but to generate levels that are tailored to particular players' playing styles and skills. The level generator gets as inputs how well a player performed on a test level and various metrics detailing e.g. how much time the player spent jumping and running and how many fireballs were fired. The level will then be judged by the player who played the test level. See more about the rules at: http://www.marioai.org/LevelGeneration/Interface Also, please note that those who submit a level generator can also submit a paper about it to the CIG conference. http://game.itu.dk/cig2010/?page_id=483

Well, there is nothing in the article (or in our research program as a whole) that says that playing styles are static. What's (more or less) static are models of player style/player preferences. Once we have the model, we can re-categorize you every time you play. This way we can do adaptive difficulty, amongst many other things.

The categories did not exist prior to the data; they were found by unsupervised learning algorithms in the data.

Can you point me to the paper? As far as I'm aware, he did a taxonomy based on qualitative observations of the game; this is a quantitative study, with the categories found purely by machine learning and a large dataset.

Personally, I love Java, but I recognize that not everybody does. As another poster has already commented below, any language is permitted as long it can somehow interface to the game code. To begin with, there are several languages other than Java that run on the JVM (Scala, for example) and these can interface directly to the code. You can also interface via the provided TCP interface; we've included a Python example. Or via JNI (Java Native Interface) for c programs.

The CIG deadline is September 3, which is almost a month away (you don't need to submit to the first phase). Plus, if the competition is a success, it will run next year as well.

While I admit the documentation is a bit on the short side, it should be perfectly enough to get started. All you need to do is look at the Agent interface, and there you have the format of the data the game is giving to you.

Yeah, please enter! You can set the level difficulty from 0 to 10, and choose whether to have enemies in or not (with the world paused/unpaused option), so basically you can evaluate your solutions first on a paused level with difficulty 0 (few holes and few obstacles) and then incrementally increase the difficulty.

We did some advertising for this within the academic research community in spring, but for various reasons we were a bit late with reaching out beyond academia. Definitely an oversight on our part. Still, the deadline for the CIG phase (you don't have to submit to the first phase) is almost a month away, and if the competition is a success this year we'll run it next year as well.