For the past few weeks, I’ve been doing extensive research on the nature of chess and the inner workings of chess AI. I think that all of the strategies that the computer uses can in one form or another be used in human play. Here is a list of some of the strategies, and a parallel to how they are used in human play:
The minimax algorithm: We can search multiple positions to a finite depth and compare the final positions and the paths taken to get there and evaluate which one maximizes our potential gain while making sure we consider the best responses our opponent has to our moves.
Monte Carlo Tree Search: When we have multiple moves that all seem to yield satisfactory results, we can use a probabilistic approach to determine which move to make. Leela Chess Zero used this approach in 2018, where she would make moves based on her probability of winning. Therefore, it would be helpful to play one type of position many times to get an idea of what moves work in the long-run and what doesn’t.
The neural network principle states that simply by playing chess, you will be able to better identify what the best moves are. Therefore, taking the time to play games and learn from your mistakes is pivotal to chess improvement.
It is these basic ideas in conjunction with consistent practice against stronger engines that will allow you to draw your games against them.
To win, however, we’ll need a method that a computer cannot outcalculate us with. The strength of the computer is that it can take your best moves and find the optimal response based on positions 50 moves down the game tree. To beat a computer, you have to think 51 moves ahead. While this is a large number, I do believe through constant practice anything can be achieved. For now, I’ll work on getting better by going one move deeper in my calculations every time.