The Logic Behind Computer Chess

Table of Contents

1. Introduction
2. The Concept Behind Computer Chess
3. The Basic Algorithm of Computer Chess
4. The Logic of Computer Chess
5. Step 1: Constructing a Tree
6. Step 2: Evaluating the Outcomes
7. Step 3: Making a Move
8. Other Functions to Evaluate Moves
9. Alpha-Beta Pruning
10. The Advancement of Computer Chess
11. Pros and Cons of Computer Chess
12. Conclusion

The Logic Behind Computer Chess

Chess is a game that requires intelligence and thought processes, so how can a computer possibly play it? A computer that is playing chess is not thinking; instead, it is calculating through a set of formulas that make it take good moves. In this article, we will explore the concept behind computer chess, its basic algorithm, and logic.

The Concept Behind Computer Chess

A human chess player starts with very limited abilities, and each defeat comes as something of a surprise and learning for a human being. Therefore, the game of chess involves a great deal of high-level abstract thought, visual pattern matching to recall board positions, rules, and guidelines, conscious thought, and even psychology. Computers do none of this.

The Basic Algorithm of Computer Chess

Chess requires intelligence and thought processes, so how can a computer possibly do it? A computer that is playing chess is not thinking; instead, it is calculating through a set of formulas that make it take good moves. Let's go through this step by step.

The Logic of Computer Chess

Step 1: Constructing a Tree

Let's say You have a chessboard set up with each player having 16 pieces, and it's the computer's turn playing with white pieces. Now, the computer can make one of 20 possible moves - two each for the eight pawns plus two each for the knights. In response to any of those moves, the opponent can make 20 possible moves. So, starting two moves into the game, we have 20 times 20 equals 400 possible sets of outcomes. Let's assume that the computer has 20 ways to respond to that opponent's move. To each of these 400 scenarios, which makes 400 multiplied by 20 available legal moves at the end of the third move. This way, the process will go on. It thinks about it in the world of all possible moves up to its program depth, and it makes a big tree for all of those moves. In theory, the perfect computer would be able to get to the very bottom of this tree and look at all possible configurations of the board - approximately 10 to the power of 120. Then it would see which are the paths down this tree that lead to its victory and choose accordingly.

Step 2: Evaluating the Outcomes

There is a big problem - 10 to the power of 120 is a very huge number. For example, there have only been 10 nanoseconds since the big bang. There are thought to be only 10 to the power of 75 atoms in the entire Universe. When you consider that the Milky Way galaxy contains billions of suns, and there are billions of galaxies, you can see that that's a whole lot of atoms. That number is dwarfed by the number of possible chess moves. We'd be sitting around waiting for the damn thing to make its move till the universe ended. So, what real computers do is build up this tree to the best of their hardware capabilities - 5, 10, 20, or whatever moves into the future. Once they have this limited tree, they evaluate each position using an evaluation function. For a really simple example, an evaluation function could be the number of pieces the computer has minus the number of pieces the opponent has. For example, the computer has 10 pieces left on the board, and the opponent has only eight. Then the computer would evaluate such a board to 10 minus 8 equals 2. Of course, that's not a very good evaluation function, but you get the idea. This can be made more and more complicated, taking into account the value of individual pieces, board position, control of the center, vulnerability of the king to check, vulnerability of the opponent's queen, and tons of other parameters.

Step 3: Making a Move

The analysis is done; it's time to make a decision. Let's make up a Simplified tree. The computer playing as white has to decide its move. It constructs the tree above and applies what is called the minimax algorithm. It starts from the bottom (assuming third level here) and chooses the one with the maximum score. Consider the leftmost circle in the Second level. It has two possible outcomes - two and eight. Since it will be the computer's turn at that stage, it chooses the best outcome, that is, the one with the maximum score, which is eight, and so it assigns eight to that node. Similarly, for all the nodes in the second level, now for the second level, the outcome is decided by the opponent since at that time, it will be black's turn. The computer assumes that black will make the move, which is best for black and so the worst for white, hence it chooses the setups with minimum score. For example, for the center note on the first level, there are three possibilities - nine, five, and nine. The computer assumes black will take the one that leaves the computer weakest, and that is five. So, the first level nodes are all given values. Finally, the first level is the computer's turn, so it makes the choice with the maximum score, which is seven.

Other Functions to Evaluate Moves

There are many other functions to evaluate the moves. Minimax is just one of them.

Alpha-Beta Pruning

By applying a technique called alpha-beta pruning, the algorithm can run about twice as fast and requires a lot less memory.

The Advancement of Computer Chess

As you can see, this process is completely mechanical and involves no thought. It is simply a brute force calculation that applies an evaluation function to all possible board configurations. Thus, the computer climbs the tree, alternatively choosing minimum and maximum scores, thus the name minimax, and makes the choice that leaves it best off in the end. The better the hardware, the deeper the depth of a tree it can analyze, and so the better its chances of winning, which is why computers couldn't usually beat humans in the 1960s but now are regularly able to thrash grandmasters.

Pros and Cons of Computer Chess

Pros:

• Computers can analyze more moves than humans can.
• Computers can play chess at a high level without getting tired or making mistakes.
• Computers can help humans improve their chess skills by analyzing games and suggesting moves.

Cons:

• Chess played by computers lacks the creativity and intuition of human players.
• Chess played by computers can be boring to watch because it often involves long sequences of moves that are difficult for humans to understand.
• Chess played by computers can be intimidating to human players, who may feel that they cannot compete with the machine.

Conclusion

In conclusion, computer chess is a fascinating subject that involves a great deal of high-level abstract thought, visual pattern matching, and evaluation functions. While it lacks the creativity and intuition of human players, it is a powerful tool for analyzing games and improving chess skills.