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Thread: Computers become champions in yet another game - poker

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    Administrator lpetrich's Avatar
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    Facebook’s Poker Bot Shows How A.I. Can Adapt to Liars - "In a new study with significant real-world implications, a poker bot crushed human pros at six-player, no-limit Texas Hold ’em"
    Sometimes, poker is all about the bluff. Make the table believe you have a full house when you really have a low pair, and it can pay off big time. Read your opponents — a grimace here, a smirk there — and bet accordingly.

    ...
    A poker-playing bot called Pluribus recently crushed a dozen top poker professionals at six-player, no-limit Texas Hold ’em over a 12-day marathon of 10,000 poker hands. Pluribus was created by Noam Brown, an A.I. researcher who now works at Facebook, and Tuomas Sandholm, a computer science professor at Carnegie Mellon University in Pittsburgh. (The two co-authored the paper in Science.)

    ...
    The game is really a simulator for how an algorithm could master a situation with multiple deceptive adversaries that hide information and are each trying to pressure the other to quit. A.I. can already calculate probability far better and far faster than any human being. But poker is as much about coping with how humans lie as it is about reading the cards, which is exactly why it’s a useful game for A.I. to learn.

    “I think this is really going to be essential for developing A.I.s that are deployed in the real world,” Brown told OneZero, ”because most real-world, strategic interactions involve multiple agents, or involve hidden information.”
    Part of that is, of course, bluffing, and the article mentions that strategy. This software does not try to read facial expressions or other such player features, and for human players, playing online means that they also play under those conditions. So it has to decide from players' betting histories.
    The new bot, Pluribus, doesn’t adapt to other players at the table — it won’t try to understand how John and Jane play the game differently. It doesn’t have a tell — a sign that they might be bluffing or in fact actually have a good hand — and it only bluffs when it’s calculated that it’s a sound strategy, statistically speaking.

    “People have this notion that bluffing is this very human thing where you’re looking at the other person and the other person’s eyes, and trying to read their soul, and trying to tell if they’re going to fold or if they’re bluffing right now,” Brown told OneZero. “That’s not really what it’s about. It’s really a mathematical thing. Bluffing is all about balancing betting with good hands with betting with bad hands, so that you’re unpredictable to your opponents.”
    So the software is capable of bluffing.

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    Administrator lpetrich's Avatar
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    Now for quantifying game-world complexity. Game complexity has a lot of numbers. I use state-space complexity, since it is a measure of how many possible configurations there can be. I also find the number of bits.
    • Tic-tac-toe: 10
    • Checkers: 65
    • Chess: 155
    • Go: 565
    • Backgammon: 65
    • Poker: (deck) 226, (hand) 21, (TxHe visible) 34


    Many video games have more realistic-looking game-world displays, and I will attempt to estimate how many bits. Game consoles have well-defined hardware, so I will use them.


    AI software has been made to learn video games, by looking at display framebuffer output and deciding what control actions to do. One can run game consoles in emulators, something that is likely faster than the originals for earlier ones.

    AI masters 49 Atari 2600 games without instructions | Ars Technica - Human-level control through deep reinforcement learning | Nature

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