191–200 of 242 posts
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I couldn't get through the cannibal part of Consider Phlebas, was soo weird.
There's one scene or so in each one of his books that's just too much for me. I just don't need to donate brainspace to that sort of thing. (Use of Weapons has one, Song of Stone too.) I like 80% of his work, 15% is a pointless depressing slog, and the other 5% is just too much for me.
Comparing against Stockfish 8 in a paper released today and labeling it as "Stockfish" is bordering on being dishonest. The current stockfish version (14) would make AlphaZero look bad, so they don't include it ...
If you are interested in this, I maintain a list of boardgame-solving related research at https://github.com/captn3m0/boardgame-research , with sections for specific games. This looks really interesting. It would be a good project to test this against a general card-playing framework to easily test it on a variety of imperfect-information games based on playing cards.
Thank you for posting! Maybe you can include the game of Arimaa [1]. Arimaa was designed to be hard(er) for computers and level the playing field for humans. Algorithms were developed eventually, though I have not kept up to know where that stands today. [1]. https://en.wikipedia.org/wiki/Arimaa
If you are interested in this, I maintain a list of boardgame-solving related research at https://github.com/captn3m0/boardgame-research , with sections for specific games. This looks really interesting. It would be a good project to test this against a general card-playing framework to easily test it on a variety of imperfect-information games based on playing cards.
I tried my hand once or twice at (re-)implementing board games [0], so that I could run some common "AI" algorithms on the game trees. What tripped me up every time is that most board games have a lot of "if this happens, there is this specific rule that applies". Even relatively simple games (like Homeworlds) are pretty hard to nail down perfectly due to all the special cases. Do you, or somebody else, have any reco…
The key is to build a data-driven state machine, rather than writing logic with a bunch of 'if' statements.
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You often don't need anywhere near the amount of compute in these papers to get similar performance. Suppose you're a business that needs to play games. Most people seem to think that it's a matter of plugging in the settings from the paper, buying the same hardware, then clicking a button and waiting. It's not. The specific settings matter a lot. But my main point is that you'll get most of your performance pretty r…
My personal experience was the opposite. I'm currently trying different approaches for building a Bomberman AI for the Bomberland competition that was discussed here on HN a few weeks ago. "IMPALA with 1 learner takes only around 10 hours to reach the same performance that A3C approaches after 7.5 days." says the paper, but I can run A3C on a cheap CPU-only server but to get that IMPALA timing, I need to spend a lot…
If you are interested in this, I maintain a list of boardgame-solving related research at https://github.com/captn3m0/boardgame-research , with sections for specific games. This looks really interesting. It would be a good project to test this against a general card-playing framework to easily test it on a variety of imperfect-information games based on playing cards.
Earlier quoted context omitted.
PSA: The "Culture" novels by Iain M Banks are fantastic and can be read in any order. "Player of Games" was the 1st one I read and still probably my favorite.
I keep hearing recommendations for the Culture books so I tried reading it recently and it just didn't work for me -- I gave up on it halfway through, which is rare for me.
This is a great result, but you can see that it's more of a theoretical case because of this: "converging to perfect play as available computation time and approximation capacity increases." That is true for pretty much all current deep reinforcement learning algorithms. The practical question is: How much computation do you need to get useful results? Alpha Go Zero is impressive mathematics, but who is willing to sp…
Is that true? I was unaware that PPO, SAC, DQN, Impala, MuZero/AlphaZero etc would all automatically Just Work™ for hidden information games. Straight MCTS-inspired algorithms seem like they'd fail for reasons discussed in the paper, and while PPO/Impala work reasonably well in DoTA2/SC2, it's not obvious they'd converge to perfect play.
If you are interested in this, I maintain a list of boardgame-solving related research at https://github.com/captn3m0/boardgame-research , with sections for specific games. This looks really interesting. It would be a good project to test this against a general card-playing framework to easily test it on a variety of imperfect-information games based on playing cards.
Imperfect information games will always have a luck element that gives casual players an edge. That's basically the appeal of card games over board games.