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I'm doing a paper for school on how computer chess programs are like 'systems' for my systems engineering class. It's classified as a 'system' because the program takes in an input (the current position) and evaluates it and then spits out an output (the next move).
I was wondering if anyone have any sources (preferably online) of any papers etc on how computer chess programs work. Thanks!
I have a copy of David Levy and Monty Newborn's 1991 book "How Computers Play Chess" -- you could borrow it if you'd like. It may also be in the Dal library.
Just drop me an email if you're interested - we could meet somewhere in town, near Dal.
http://www.math.ucla.edu/~tom/papers...riegbishop.pdf is a sort of interesting way to apply mating nets for kriegspiel, a neat variant of chess. I believe that they talk a bit about generalized sized boards. For other elementary endings, they often have what mathematicicans in combinatorics have built something similar to a graph of positions for storing small pieced ending tablebases, & this too might be an interesting implementation attempt. I think there have been a few other kriegspiel mating net endings attempted, too.
The subject of 2-movers is fairly rich, even the ones that don't begin with a check. 3 & 4 mover studies often have theme classifications or extra elegance points for things like pure or model or ideal mates, something that an english-commentator engine like fritz won't always tell you to look for when you are composing during a real game after analysing in themed english-rely mode.
Studies often take themes to the extreme, where a whole line, not just a starting move, is emphasized for finding the extra hidden value of finding a human-type idea or exception to a typical mating theme idea.
Then again, there's also simpler ideas to look at, such as common demolition sack patterns vs an unsafe middlegame castled King. Construction of a tree or transposition tree (incl. (i) move-orders that transpose to a line in a different number of moves ie the Sveshnikov Bf4 vs Bg5 theoretical idea to get to a common position in like either 8 or 9 moves (also seen in some russian lines vs French exchange lines) & (ii) colour reversal transpositions, where one switches sides in something like an IQP position, by losing a tempo to transpose) is another idea to look at, but this idea seems to be commonly covered, since opening ideas are more frequently needed to be navigated more carefully than ending ideas (one might not always get to an ending if they cannot survive an opening or midgame mating sack).
Ken Thompson is a big name in the theory & you can check the profile of RandomVisitor's page on http://www.chessgames.com , if you have an account there. He's running an 8-core machine to help analyse their team chess game currently running. You might also want to look at the term centaur chess tournament (a human + laptop assisted vs human + another laptop assisted game of chess at quicker time controls, a sort of rapid version of how the chessgames game is going - 1 move/player each 2 days is more of a corresondence rate, so here they have time to do a deep 30 ply exhaustive search ahead, or so they claim, of a main line) & how the various analysis modes of common programs currently run for evaluation (blunder-check often returns much different results than a 1 hour deep analysis or the on-the-fly kibitzing modes).
Finally, Darse Billings of U of Alberta games group at CS dept there (with guys like Schaeffer, etc who tried solving draughts & poker) had done a couple of articles for http://www.chessbase.com that involved what risk a computer should take for maximizing its turnament cross-table strategy of winning something like rating points or a prize or trophy. A conclusion seemed to be what humans knew for a while after running a few simulations: the player who plays to play risky (to try to score the full point, even if it means risking a loss of a full point), is more often likely to reap benefits than the conservative computer player ie nobody ever won a tournament by merely trying to draw all their games, all the time.
Such are some of the issues that computers face for being helpful playing & analysis tools.
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