The Closed Sicilian is another solid choice for black that I think may be underestimated. It avoids a ton of theory, but unlike most "anti" openings, it is perfectly normal and does not involve any gambits or strange moves. One of the best benefits of using this variation is that while white's position is usually more cramped than usual, black does not have an open C-file, so white really doesn't have anything to worry about. Even though all the critisizm given to this variation's many weaknesses is completely true, there's no reason to worry because black doesn't have any conterattacking chances until well after white is developed and has a solid position.

I think a key mistake of humans is that we focuse too much on contolling the center, getting the bishop pair, etc., which are all things that practice and computer analysis have shown to usually be advantages. But a computer's goal in the openings is not to acheive any of this, just to win the game. If it sees that in thirty moves it will need to have control of a certain square, it controls it, not because it's a center square but because in that certain game it needs to. The problem is that usually the squares a computer needs to control are ones in the center, because of how the board is set up, so humans think that the goal of the opening is to control the center, not to work towards mate. Humans take things that usually get advantages and think that in all games they will help. Many times in the opening stage I find myself trying to control every center square, and when I do I am satisfied. But I am not looking at what is actually going to help me checkmate the king, so many of those dominated squares in the center don't end up mattering at all. In fact I don't even look into the future when playing the opening anymore, but instead focuse on pawn structure, center squares, etc.

As others pointed out, you have this entirely backwards. Humans have been working on this opening for at least 157 years. Collectively, our minds are the most massive and powerful thinking tools on this planet. Through game after game of trial and error, we have figured out what does and doesn't work in the opening. Theory is constantly refining lines, but the refinement is starting at deeper and deeper points.

Computers, on the other hand, are looking relatively shallowly at the position. You can let your computer work on this position for an entire week and it simply can't look enough ply deep at a time to get an accurate idea of how the moves it plays early impact the situation much later on. There is too much complexity in the game from the start to expect several hours to be sufficient to simplify it.

How do you think main lines became main lines? Years of trial and error and analysis. We've determined that there are easy ways to handle deviations, just as the computer would.

<And the thing about computer engines evaluating the way we tell them to is not entirely true. The best computers are simply told the rules and evaluate, having to find the relative value of the peices and positions by themselves. Even better are computers programmed with Artificial Inteligence, which are very accurate and involve no human interference. >

<No computer can look at a position and instantly relize they know that position> This may be true for most computers (soon all will be able to do this) but this is not actually a disadvantage. A computer will look at a position and find the same 'best' move every time, so it does not need to remember it.

<Another thing humans have is the ability to break down a position logically> This is not a strength, but a weakness. Of course, when a game is played with human vs. human, this gives an advantage to the one who's better at this, but against computers it's a disadvatage. A big problem with humans is that we set short-term goals in a chessgame: often in the opening we try to get something, and in the middlegame the strategy is usually to attack a certain peice or area. Computers on the other hand do not break down the game into smaller segments, but look at chess as one game. They are inbiased evaluaters because they look at short-term and long-term weaknesses, and also how to exploit them, or if they are exploitable.

I believe that engines actually look at the position of the pieces before giving them value. Also, position pluses of pieces (blockades, squares they control, etc) are taken into account. So having a knight is worth something, but what it's doing where also matters. The engines take care of everything, so all is good with them.

So I was doing some research to find what you were talking about in terms of AI. I found that there are engines that can learn relative values of pieces and other featurs of the game just by playing. I was wrong in that they do take not so long to become very good. However, I was right in that they still aren't as good as the more traditional engines. The one paper I was looking at had an engine that improved 400 points by playing, yet it was still hundreds of Elo points below Rybka.

When you said <Even better are computers programmed with Artificial Inteligence, which are very accurate and involve no human interference.> what did you mean? You said this after mentioning the ones that self-learn the values, implying that these engines are learning more than that. What sort of AI are you refering to? Can you find me a paper or a name I can look up or anything? As far as I knew, no engine that didn't simply build the game tree and then search through that was any good.

<Also, chess is not as complicated as you think for computers. For humans it is because of all the things required to learn and all the years of practice it takes to get good, but our brains learn chess very slowly (through an inefficient sort of trial and error system). For an AI engine though, it does not take very complicated programming to teach it chess, and all the rest it does by itself.>

<I believe that engines actually look at the position of the pieces before giving them value. Also, position pluses of pieces (blockades, squares they control, etc) are taken into account.> That is completely true, but it still does not make the evaluation extremely accurate, because there are some constants programmed into the computer that don't change with each position. The problem with evaluations like these is that they only look at what peices are doing now, and not what they will do later. A bishop that is pinning a knight may look "valuable" to a computer, but will it actually end up impacting the game? Often the answer turns out to be no. This is what experienced AI engines can know from practice.

<they still aren't as good as the more traditional engines.> I guess that was more something I was saying for the future. However, I wasn't talking about the AI engines that actually play the game, I was talking about the ones that evaluate and are used for finding strengths of openings, etc. These are good because they don't assume optimal play, but instead practices through any lines that get through the filter, so the evaluations are good for measuring how the openings or positions will do with only mediocre human play as opposed to near-perfect computer play.

<What sort of AI are you refering to?> Once again I'm not refering to chess engines that publically play, but private ones that are built by computer-scientists to evaluate chess. You might be able to find some info on these on college sites, where I've heard some have been programmed.

<Chess is huge.> This is absolutely true. We are many, many, many years away from the day when somebody will finally be able to build a chess engine to go through every line until checkmate or draw and find the absolute optimal move in every position (I can't wait to see what the true best first move is!). In fact, I doubt this will ever happen: I read on one site that the number of possible chess games is greater than the number of atoms in the entire universe. By then, I'm sure there will be no point to chess anymore, because everyone will know the best response to everything. But in the present, a good filter solves most of this problem, and also AI engines will stop a line if one person has such an advantage that it calculates there is no way for the other person to win, so between those two things and some other preprogrammed factors it greaty cuts down on the amount of lines it has to go through (still a lot of course).

By the way, when I googled "artificial intelligence chess engines" I got some interesting sites, if you're looking for real-life examples of these engines.

I don't think this is correct. The engines I looked at that involved self-learning would have all of the evaluation function set in place with only the values (the eventual 'constants') changing based on the learning. But for the computer to decide how valuable the bishop will be (as in 'in the future of the leaf node of its evaluation') requires the computer to be able to decide what it will do in order to evaluate. You seem to be giving AI too much credit -- you can't just say 'AI' and then assume anything is possible.

<<they still aren't as good as the more traditional engines.> I guess that was more something I was saying for the future. However, I wasn't talking about the AI engines that actually play the game, I was talking about the ones that evaluate and are used for finding strengths of openings, etc. These are good because they don't assume optimal play, but instead practices through any lines that get through the filter, so the evaluations are good for measuring how the openings or positions will do with only mediocre human play as opposed to near-perfect computer play.>

So you're saying that you can find better openings by assuming your opponent will play worse moves? This seems to require some logical leap that I am not willing to take. If you make a worse move than the best available hoping to trap your opponent, and then he doesn't fall for it, you are now worse off than you would have been. This sounds bad.

<<What sort of AI are you refering to?> Once again I'm not refering to chess engines that publically play, but private ones that are built by computer-scientists to evaluate chess. You might be able to find some info on these on college sites, where I've heard some have been programmed.>

<False. They have no idea what a best move is.> That is true, but I didn't literally mean they will find the best move. I meant that depending on how they will evaluate positions, they are going to think a certain move is the strongest every time, so they will play that same move every time even though they don't "remember" it.

<<A big problem with humans is that we set short-term goals in a chessgame: often in the opening we try to get something, and in the middlegame the strategy is usually to attack a certain peice or area.> False.> How can you say false?? Just take a look at some of the HUMAN MADE French lines, and you will see that they often obsess on attacking the d4 pawn, which they well know isn't going to get them any big advantage.

<<it always ends up exploiting minor weaknesses that no human would have payed attention to> False.> I was just saying that if the computer lets you get a strangly good position in the opening, it's not just playing stupid, in probably has something else in mind.

<The engines I looked at that involved self-learning would have all of the evaluation function set in place with only the values (the eventual 'constants')> The ones I've heard of do not have the peice values as constants (that are changing), but instead have a formula in place which is made up of the parts that give a peice its value (number of squares attacked, number of squares defending, mobility, escape options, etc.) and the computer "learns" how much each of these factors impacts each peice, and then finds the value of peices in each situation, so the peice values are never constants, but more accurate depending on the situation.

We're saying the same thing, though maybe I didn't make it obvious enough. However, note that with what you are saying now, the computer is basing it's view of the value of a piece only on certain predetermined factors. It cannot make up what factors it wants to consider, so it would see a bishop executing a pin and be happy. How is it able to guess how useful that will be in the long run?

<<So you're saying that you can find better openings by assuming your opponent will play worse moves?> No, I'm not saying that at all. What I'm saying is that I can safely assume that no human that I play will ever find a move that gets an advantage in every line after 15 perfect moves, which a very strong computer might (but probably won't) see. Instead, humans will only play according to what they see in the near future, or advice they have learned (like reading in an opening book that a certain move will give an endgame advantage).>

So what's the vaule in your statement? Are you actually considering playing less than the best move at some point hoping your opponent will make an even worse move back? How can you plan ahead and make moves without assuming your opponent will play back perfectly?