Even if I must admit I was quite deceived by IA results... (except in speech recognition)
I still believe that things remain to be found.
I can't believe that with the computers, the language, the theory we have now,
we aren't able to do something more efficient...
 
To my mind you should mix different methods depending on your needs.

Here are some methods I like to use :

• Genetics Algorithms.
  Each time I read 'survival of the fittest' it comes to my mind.
  Each time I've to tweak/optimize complex function that I can "rate" the result I choose this techniques too...
  Although transcoding your parameters for efficient crossing-over may be a REAL task...
  (see Masem's work : Algorithm::Genetic or the infamous Genetic Programming or breeding Perls)
  
  
• Neural networks.
  To classify complex objects (picture,sound).
  To create simple semi-adaptative behaviour.
  (tons of links on the web, here is start, CPAN has some modules too...)
  
  
• Computationnal Optimization(Simplex, simulated annealing...)
  Not really IA, but I like to combine with others techniques.
  (See Math::Amoeba for the simplex)
  
  
• "Inferential Theory of Learning" from Michalsky
  He did a wonderful job in formalizing the "learning process"...
  Every symbolic learning engine could take benefit of it...
  
  
• Sorted Alpha-Beta.
  For turn by turn process, with only one action at each step (chess play...)
  

Now speaking about Perl, I think that knowledge and logic (predicate) are well suited to be described with string,lists,hash...

Please tell us informed about what you do, beccause it's a very interesting topic to my mind...

UPDATE : shotgunfx comment lead me to add the alpha-beta entry...

"Only Bad Coders Code Badly In Perl" (OBC2BIP)

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