Original post by Anonymous Poster
So far my experience in this arena is indeed that there are very few articles that I can easily relate to
Most get way too philosophical or academic about it (like many have mentioned so far)
That tends to be because academic articles assume a certain level of education. While I know this sounds snobbish, it''s not. Academic publications must be kept short (due to publishing costs). The typical length is about 6-8 pages for conference articles and 10-12 for journal articles. In this limited space you don''t have room to provide much background instruction so, as an academic writer, one must assume that the reader has some level of knowledge. Yes, this makes the paper less accessible, but the point of the paper is not so much to educate as to inform. Education comes from books, courses and discussions with academics.
Until recently there hasn''t been much call for AI books for those outside of academia (other than general interest books). There are more titles on the shelves now and more in the future. Heck, maybe I should jump on the band wagon and put my PhD to some use... I''m sure it would enable me to sell a half dozen copies at least!
Original post by Geta
How would one go about implementing effective Plan Recognition and accurate Opponent Modelling for games like Age of Kings or Empire Earth which must operate in real time, with upwards of eight computer or human opponents, and hundreds (if not thousands) of agents to manage? And be able to process on a typical consumer system?
As usual, you''ve it the nail on the head!
This is the ONE reason why most interesting/advanced AI techniques have not yet made it into the commercial arena: lack of processing power available for real-time analysis.
However, more and more graphics functions have been moved to the GPU, freeing up the CPU for AI... and machines are getting faster (although that doesn''t mean machines in homes are getting faster). What we can hope for in the short term are ''pared-down'' versions of some of the bigger algorithms. Opponent Modelling of a single player in real time is certainly achievable today. Plan Recognition techniques are also practical in certain domains.
Remember though, that techniques such of these don''t have to run once per frame as they are strategic level algorithms. Opponent Modelling can take place over minutes or even hours of gameplay; the same with Plan Recognition.
Tactical level algorithms are another story obviously. In this regime I believe that reactive, nonlinear behaviours built up from basic building blocks will lead to the most interesting behaviours in the short term. As hardware gets faster and memory gets cheaper it will be possible to do more in the shortened time frames of this regime (for example, you will eventually be able to run a couple of dozen of John Laird''s Quake bots on a single machine, rather than having a few connecting from external machines via the port interface).