AlphaGo is a computer program that plays the board game Go.
It was developed by DeepMind Technologies which was later acquired by Google.
Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the name Master.
After retiring from competitive play, AlphaGo Master was succeeded by an even more powerful version known as AlphaGo Zero, which was completely self-taught without learning from human games. AlphaGo Zero was then generalized into a program known as AlphaZero, which played additional games, including chess and shogi.
AlphaZero has in turn been succeeded by a program known as MuZero which learns without being taught the rules.
AlphaGo and its successors use a Monte Carlo tree search algorithm to find its moves based on knowledge previously acquired by machine learning, specifically by an artificial neural network (a deep learning method) by extensive training, both from human and computer play.
A neural network is trained to identify the best moves and the winning percentages of these moves.
This neural network improves the strength of the tree search, resulting in stronger move selection in the next iteration.
