Hey, really cool, I didn't know about this! True, the games weren't any fun to play, but it's a nice experiment.

One major difference is of course that the evolution shmup is an example of interactive evolution: the human is used as a fitness function. Instead, we use another evolutionary algorithm as fitness function.

You're of course right that it would be very close to impossible to come up with all the references etc. in a game such as nethack. For this, we need humans!

However, such a thing as finding sensible values for how much money you get for selling something or how much damage you can do by hitting someone is definitely something you can optimize. Automatic game design, using evolutionary algorithms, is _not_ the same as arbitrarily making rules, as we are actually testing the rules with another learning process!

There are many proposed methods of measuring emergence - maybe none of them is very general, but I think there's a few that might be useful for specific domains such as games. I need to look into this. You're right, it's not trivial!

Yes, I'm the one that did the experiments and wrote the paper.

No, we didn't use humans to test the games. We used evolutionary algorithms.

In fact, one of our main inventions is the idea of using a learning algorithm to grade the game, based on the idea that learning equals or creates fun. There are many other "static" functions for measuring fun proposed already, measuring things such as balance or challenge, but we are the first to use learnability as a predictor of fun.

Nethack is on the list. Seriously, I think it's one of the things that's actually plausible. At least until I'm disproven!

Yes, in practice we're envisioning this to be used mostly for improving on given game templates, but one has to try to do the near-impossible as well, doesn't one?

Alright, I'll try that some day. You know, it's not always that easy to get research funding through trying to be original and relevant, so maybe your method is better.

You are right, we are oversimplifying. But we need to start somewhere, don't we? I think that those things you measure (humour, likes and dislikes, genre change) will be very hard to measure/create automatically, but not necessarily impossible.

Interesting post. Actually, what we're trying to capture is the "real" sentiment of progression, where you get better at playing the game. But it seems perfectly doable to capture the "level of emergence" as well, via some entropy measure or somesuch. I'll think more about this...

This is a good point. In fact, there is research on identifying different player "stereotypes", and having ways of automatically identifying what stereotype a player belongs to could enable us to automatically create games for particular players. Or just adapt a given game so it suits some player better.

Or try his newer book, "Pattern Recognition and Machine Learning". ( http://research.microsoft.com/~cmbishop/PRML/index.htm ) If you have a background in physics, it's an excellent book. Or so they say, I find it too mathematical myself. But Russell and Norvig is also an excellent choice.