Chapter
How Monte Carlo Tree Search Works in Board Games
45:22 - 54:10 (08:48)
Monte Carlo tree search, which involves thinking several moves ahead in a game, can be successful in perfect information board games like chess and Go, but is not as effective in games like poker.
Clips
Advancements in artificial intelligence have made it possible to create poker bots that can easily outsmart humans.
45:22 - 48:04 (02:42)
Summary
Advancements in artificial intelligence have made it possible to create poker bots that can easily outsmart humans. These bots have been developed to play various forms of poker, and with enough effort, it's possible to develop an AI that can beat humans at every variant of the game.
ChapterHow Monte Carlo Tree Search Works in Board Games
Episode#344 – Noam Brown: AI vs Humans in Poker and Games of Strategic Negotiation
PodcastLex Fridman Podcast
Monte Carlo tree search is a vital component for AI systems like AlphaGo to make better decisions and beat top humans at games like Go.
48:04 - 51:15 (03:10)
Summary
Monte Carlo tree search is a vital component for AI systems like AlphaGo to make better decisions and beat top humans at games like Go.
ChapterHow Monte Carlo Tree Search Works in Board Games
Episode#344 – Noam Brown: AI vs Humans in Poker and Games of Strategic Negotiation
PodcastLex Fridman Podcast
In perfect information board games like chess, AI search algorithms can be successful, but in games like poker, intuition and human decision-making are crucial factors.
51:15 - 54:10 (02:55)
Summary
In perfect information board games like chess, AI search algorithms can be successful, but in games like poker, intuition and human decision-making are crucial factors. AI can use Monte Carlo rollouts to simulate game outcomes, but it cannot replace human intuition.