Re: Any chess players here?
Posted: Tue Aug 21, 2018 5:30 am
I don't think that's just Americans, Schol. Most people think even the rules of chess are too complicated to learn and they never even try.
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I think, the basics are covered by lots of practice. It's a skill.StudlyCaps wrote:I think Solar is right, I know the rules just fine, and I think most people do. But I'd never call myself a chess player because even the basics of chess strategy are a total mystery.
For chess, "strategy" is mostly basic game theory. Start by analysing the board and giving it a rating (e.g. maybe based on how defended/exposed your pieces are, how defended/exposed your opponents pieces are, how many of your opponents pieces can be attacked, how many of your pieces opponents can attacked, and possibly with an underlying "this type of piece is worth this many points" system). Then for each move you can make, see if it improves the board rating, recursively (e.g. for each move you can make, for each response opponent can make, for each move you'd be able to make after that, ..). Finally make the move that improves the board rating the most.StudlyCaps wrote:I think Solar is right, I know the rules just fine, and I think most people do. But I'd never call myself a chess player because even the basics of chess strategy are a total mystery.
I think the new "generation" of AI chess algorithms are a little different. Rather than pre-defining the "weight" of the pieces and the squares, I think they look at the board more like a bitmap image, and break it down into patterns, kind of like a facial-recognition, or object-recognition algorithm.Brendan wrote:Hi,
For chess, "strategy" is mostly basic game theory. Start by analysing the board and giving it a rating (e.g. maybe based on how defended/exposed your pieces are, how defended/exposed your opponents pieces are, how many of your opponents pieces can be attacked, how many of your pieces opponents can attacked, and possibly with an underlying "this type of piece is worth this many points" system). Then for each move you can make, see if it improves the board rating, recursively (e.g. for each move you can make, for each response opponent can make, for each move you'd be able to make after that, ..). Finally make the move that improves the board rating the most.
Of course when computers do it it's typically "brute force search with optimisations";
From the paper (linked to from the article you mentioned); it looks like its almost exactly the same as what I described except for the addition of a neural network to auto-guess various parameters (the weights of the pieces, etc); mostly by playing lots of games against itself and while using a neural network to tweak a formula used to calculate board ratings.SpyderTL wrote:I think the new "generation" of AI chess algorithms are a little different. Rather than pre-defining the "weight" of the pieces and the squares, I think they look at the board more like a bitmap image, and break it down into patterns, kind of like a facial-recognition, or object-recognition algorithm.Brendan wrote:For chess, "strategy" is mostly basic game theory. Start by analysing the board and giving it a rating (e.g. maybe based on how defended/exposed your pieces are, how defended/exposed your opponents pieces are, how many of your opponents pieces can be attacked, how many of your pieces opponents can attacked, and possibly with an underlying "this type of piece is worth this many points" system). Then for each move you can make, see if it improves the board rating, recursively (e.g. for each move you can make, for each response opponent can make, for each move you'd be able to make after that, ..). Finally make the move that improves the board rating the most.
Of course when computers do it it's typically "brute force search with optimisations";
Here's a high level overview: https://www.sciencealert.com/it-took-4- ... -alphazero
But essentially the bottom line is that the new AI designs do not assign any "values" at all. They let the AI come up with it's own solutions. And the game that it's playing doesn't really matter. It would learn chess and checkers just as well, with no code changes. (Other than maybe the rules of the game.)
This is an extremely simplistic assessment. What makes strong chess players "strong" would be an active field of research because no one really knows it. Even with lot of games or even a lifetime of play many people do not break their class. It is lot of hard work to improve at chess after you have hit a plateau ( just like music ). You also discount game psychology. Some people are simply naturally strong in this area. Young American chess player Daniel Naroditsky has done some good work in this area. I am talking mainly about human aspects of chess above.The main differences between beginners and experienced chess players is "depth of recursion" and how accurate their estimations/assumptions/feelings are.
As far as a "game" can have practical value..Brendan wrote: Note that for computer chess engines the goal should be to lose about 75% of the time (e.g. by estimating the opponents skill and adjusting the AI's "search depth" to suit). It's far too easy for stupid people to write chess engines that ruin the game for humans (guaranteed failure is not something most humans enjoy). With this in mind, except for a little publicity ("Our unenjoyable failure beat their unenjoyable failure!") Google's research has no practical value.
People's online or real-world behavioral patterns. Feeding directly into insurance rates, chances of employment, or attention by the police / intelligence agencies.SpyderTL wrote:But it's more exiting (slash terrifying) when you start to apply this to other problem domains, like military strategies, or traffic patterns.