I assume the orginal poster meant to evolve programs before the contest. It comes up with ways to win that normal people wouldn't even think of).Īnyway, my name is Josh Marotti, so email me at and I'll get back to you on it. He let it run on his machine for months, and I think the end result even beat him using the most obscure technique you could think of (which, IMHO, is the coolest part of genetic AI. He graphed all their behavior (in gnu-plot) and there was VERY noticable steps when one made a genetic breakthrough. once removed, it was genetically altered 1 line, then reintroduced. Each bot had 5 chances to win before becoming removed. Anyway, IIRC, he pitted two different bots together and fought them.

it was a linux game (something to do with hacking memory? I never played it, so I can't remember the name). I know it was a game where two bots fought one another.

I'll email him and see if he'll release the whole project out so you can read it. Geez, it was years ago, but he's an open-source type of guy (actually he introduced me to /. What's the best way to combine two bytecode programs to produce offspring which are similar-yet-different, and have the best chance of doing well? You would obviously want a chance of mutation (possibly reducing over time). Eventually, however, you'd get a program which would at least not produce errors, even if it did nothing productive like moving and shooting. Obviously, the first N iterations wouldn't be syntactically correct bytecode (though I would suggest that you hard-code the 0xCAFEBABE prefix) and would probably throw exceptions almost immediately (resulting in disqualification). Breed well-performing bots with each other by combining bytecode together in various fashions. Generate random Java bytecode, run it as a bot, look at the points scored. In this case, the 'arena' program which pits the bots against each other serves very well. Why not do it genetically? I'm not an expert on the subject, but the way I've always thought is that it's clearly possible to 'breed' programs to do a particular task, as long as the task is very 'ratable', i.e., you have a separate algorithm which will evaluate how well the program did. C++ is a good general purpose choice for these, but each needs to be looked at individually. These are generally heavy duty specialized systems that can be very data and processor intensive. C++, or C# might be good candidates.Ħ) High level applied systems, lumping in natural language processing, vision recognition, etc. Very object like, I can hardly think of a better language than Java for it's simplicity. Lisp, java good for this.ĥ) State machines. Used to categorize information for retrieval based on a information given by the user. Lisp, Java, SQL, Perl can be good here.Ĥ) Expert systems. The construction of the database and pattern finding is intensive, but use typically is not. These are good at cross referencing data by finding subtle patterns in it. C++, Java are pretty good languages.ģ) Pattern matching knowledge databases. Good for things like handwriting recognition. These are used to pattern match and generalize. There is a rather large family of these also, existing in both asynchronous and synchronous forms. These can take up alot of processing power, or not, depending on their complexity. C, C++, Assembly are typically good languages.Ģ) Neural networks.

This type of AI is usefull fr finding a near optimal solution in a complex but static problem space, ie What is the best wing design for this airplane. Once they are done though, the end result is generally small and fast. Good ones take massive processor power to come up with. Each language is an abstraction of the problem space you are working in, so rather than making a blanket statement about a good language for AI, you should pair up the AI your talking about with a preferred language.ġ) Genetic algorithms are probably in the category you are talkng about. There are many types of AI, and many languages to do them in.

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