It uses a variant of minimax game-tree search, which is a pretty classical approach to game AI. The particular one it uses is MTD-bi, which is in the MTD family of search algorithms, related to: https://en.wikipedia.org/wiki/MTD-f
It's debatable if this is AI but basically it looks at all the moves you can make then all the responses an opponent can make and then what you can do back etc. and attaches scores based on pieces taken and the positions. And then goes with the best score.
Conventional chess programs use a similar algorithm and ones like AlphaZero are similar except they use machine learning and neural nets to judge how good positions are rather than a simple point score system.
I remember trying to write a similar one after seeing the algorithm explained on the TV show Tomorrow's World, around 1980. (Here they are explaining the cutting edge of mobile phones in the day https://www.youtube.com/watch?v=vix6TMnj9vY&feature=youtu.be...)
Notably, Stanislaw Lem mentioned eye-tracking studies by Tikhomirov which suggest that human players also do some sort of tree-walking (or whatever the proper term is), but are unable to describe it consciously.
Just to add to your reply, alpha0 and Leela actually learn to play by playing with itself, which is much more than just having a score for a given position. Also, in the chess community, Stockfish is not seen as AI, but Leela and A0 are.
> Also, in the chess community, Stockfish is not seen as AI, but Leela and A0 are.
That's a strange statement. For one, because chess players are hardly judges of what is and what isn't AI.
Among chess programmers there may be such an opinion here and there, but originally chess was a classical AI topic and alpha/beta search a classical AI algorithm. As are neural networks and Monte Carlo tree search. So it's quite a strange opinion, IMO.
AlphaZero uses a neural network to represent the probability of winning a game in a given state and a probability distribution of next moves, but it still “just” uses Monte-Carlo Tree Search (MCTS) to look for the strongest position to play based on the estimated score for each possible state. In that way it is identical to earlier agents whether using minimax, negamax/principal variation search, or MCTS. The primary improvements of AlphaZero are learning without bootstrapping from human play & using the neural network to output the probability distribution over the available moves in each state to guide the search rather than using a static heuristic (like killer move or UCB, etc).
The original AlphaGo paper even mentioned that they tested the bare neural network predictions against the version with MCTS guided by the network and found that the MCTS version won 100% of the time, which strongly suggests that search is an indispensable part of strong AI performance in games.
AI definitely doesn't imply Artificial General Intelligence, but I'm pretty sure no-one's saying that. The common confusion is between AI and sexy Machine Learning.
It uses a variant of minimax game-tree search, which is a pretty classical approach to game AI. The particular one it uses is MTD-bi, which is in the MTD family of search algorithms, related to: https://en.wikipedia.org/wiki/MTD-f
It's debatable if this is AI but basically it looks at all the moves you can make then all the responses an opponent can make and then what you can do back etc. and attaches scores based on pieces taken and the positions. And then goes with the best score.
Conventional chess programs use a similar algorithm and ones like AlphaZero are similar except they use machine learning and neural nets to judge how good positions are rather than a simple point score system.
I remember trying to write a similar one after seeing the algorithm explained on the TV show Tomorrow's World, around 1980. (Here they are explaining the cutting edge of mobile phones in the day https://www.youtube.com/watch?v=vix6TMnj9vY&feature=youtu.be...)
Notably, Stanislaw Lem mentioned eye-tracking studies by Tikhomirov which suggest that human players also do some sort of tree-walking (or whatever the proper term is), but are unable to describe it consciously.
Just to add to your reply, alpha0 and Leela actually learn to play by playing with itself, which is much more than just having a score for a given position. Also, in the chess community, Stockfish is not seen as AI, but Leela and A0 are.
> Also, in the chess community, Stockfish is not seen as AI, but Leela and A0 are.
That's a strange statement. For one, because chess players are hardly judges of what is and what isn't AI.
Among chess programmers there may be such an opinion here and there, but originally chess was a classical AI topic and alpha/beta search a classical AI algorithm. As are neural networks and Monte Carlo tree search. So it's quite a strange opinion, IMO.
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AlphaZero uses a neural network to represent the probability of winning a game in a given state and a probability distribution of next moves, but it still “just” uses Monte-Carlo Tree Search (MCTS) to look for the strongest position to play based on the estimated score for each possible state. In that way it is identical to earlier agents whether using minimax, negamax/principal variation search, or MCTS. The primary improvements of AlphaZero are learning without bootstrapping from human play & using the neural network to output the probability distribution over the available moves in each state to guide the search rather than using a static heuristic (like killer move or UCB, etc).
The original AlphaGo paper even mentioned that they tested the bare neural network predictions against the version with MCTS guided by the network and found that the MCTS version won 100% of the time, which strongly suggests that search is an indispensable part of strong AI performance in games.
There is an simple AI version with facebook fastText: https://github.com/thomasahle/fastchess
Strictly speaking, this is an AI. AI does not imply AGI.
Typically, chess engines are built on minimax, with optimizations like alpha beta pruning.
AI definitely doesn't imply Artificial General Intelligence, but I'm pretty sure no-one's saying that. The common confusion is between AI and sexy Machine Learning.