lpetrich
Contributor
Board games have long been used as testing ground for artificial intelligence. Their game worlds are simple, abstract, and stylized, but that simplicity disguises a large amount of complexity. Game complexity has some numbers, and they get very large very quickly. The smallest of them listed, tic-tac-toe, has several thousand possible positions.
Go (game) is especially difficult. Players alternate placing stones on a 19*19 board, and they try to surround each other's stones. This game has been very difficult for artificial-intelligence software to play. But DeepMind, a subsidiary of Alphabet, Google's parent company, has come up with some software called AlphaGo that has done remarkably well at this game.
Since then, the DeepMind people have devised AlphaZero, a version that can play other games, like chess and shogi, a Japanese chesslike game.
AlphaZero Crushes Stockfish In New 1,000-Game Match - Chess.com presents some of the games and analyses of them by some chess masters. In their estimation, AlphaZero had some rather interesting strategies.
CCC: Computer Chess Championship - Chess.com has a chess-engine tournament with several engines paying, including Stockfish though not AlphaZero. It does include LCZero, however, an effort to imitate AlphaZero's success.
Go (game) is especially difficult. Players alternate placing stones on a 19*19 board, and they try to surround each other's stones. This game has been very difficult for artificial-intelligence software to play. But DeepMind, a subsidiary of Alphabet, Google's parent company, has come up with some software called AlphaGo that has done remarkably well at this game.
- Mastering the game of Go with deep neural networks and tree search | Nature (2016) -- it learned on its own and also used games between human Go masters. It beat all other Go-playing software and it succeeded in defeating the European Go champion 5-0.
- Mastering the game of Go without human knowledge | Nature (2017) -- it learned from scratch by playing against itself repeatedly, and it beat the previously-described version.
- Self-taught AI is best yet at strategy game Go : Nature News & Comment -- good background article
- AlphaGo | DeepMind -- at DeepMind itself
Since then, the DeepMind people have devised AlphaZero, a version that can play other games, like chess and shogi, a Japanese chesslike game.
- [1712.01815] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm -- complete with defeating world-champion software
- A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play | Science
- Move over AlphaGo: AlphaZero taught itself to play three different games | Ars Technica
- Google's AlphaZero Destroys Stockfish In 100-Game Match - Chess.com -- Stockfish is a champion chess engine
Poker is one contender for future AIs to beat. It's essentially a game of partial information—a challenge for any existing AI. As Campbell notes, there have been some programs capable of mastering heads-up, no-limit Texas Hold 'Em, when only two players are left in a tournament. But most poker games involve eight to 10 players per table. An even bigger challenge would be multi-player video games, such as Starcraft II or Dota 2. "They are partially observable and have very large state spaces and action sets, creating problems for Alpha-Zero like reinforcement learning approaches," he writes.
AlphaZero Crushes Stockfish In New 1,000-Game Match - Chess.com presents some of the games and analyses of them by some chess masters. In their estimation, AlphaZero had some rather interesting strategies.
CCC: Computer Chess Championship - Chess.com has a chess-engine tournament with several engines paying, including Stockfish though not AlphaZero. It does include LCZero, however, an effort to imitate AlphaZero's success.