I think that what makes these games beatable repeatedly is that they're static. Not saying an algorithm properly trained won't play better than the average player a game like MtG, or my own https://aethersummon.com (specially now while it has under 90 possible scrolls only) but if you have a regular release cadence (say weekly or bi-weekly) of relevant new "cards", then I think the playing field is much more even for humans.

Those new additions can invalidate the whole training data by a single new "card" that changes completely the dynamics and would be easy for a player to understand and incorporate but not for an algorithm (perhaps with enough compute to re-train it regularly it could) - that along with the decision trees being orders of magnitude deeper, wider and with more conditionalities than go, chess or stratego - even through the same turn with the same cards available and same table state - would probably pose much harder problems for a compute bound algo.

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Arainach 3 hours ago | parent | next [-]

> Those new additions can invalidate the whole training data by a single new "card" that changes completely the dynamics

This doesn't follow. You're basically proposing that new combo decks be added all the time, and it's far simpler for an agent to scan the new cards for potential interactions with the thousands of other cards in circulation than for a human to remember all of them.

Your analogy is akin to saying that all you have to do is keep landing new code all the time, and since the agents weren't trained on the code they won't be able to identify and respond to security vulnerabilities in it as fast as humans, which hasn't turned out to be correct

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hnedeotes 2 hours ago | parent | next [-]

No, well, in MtG you could interpret it as meaning such but what I mean is that if in the training set sequence A-B-B-A when state is C-A-X-Y is the play 80% of the time, then you have a new card (that doesn't need to be combo) that by sheer mechanics thwarts that then that strategy won't stick by the addition of that single card to the opposing deck (that you can't know if your opponent is playing or not) and having one or 2 or 3 or 10 different cards renders every calculation very problematic as a play can be the best or the worst depending on such simple things diluting further the best play as the pool grows. Then you need to take into account in MtG shuffling and drawing. I think it's fair to say it's much more difficult to model... And while an agent can learn new combos, you just need to read the card once, the agent needs to be retrained.

	▲	ironSkillet 44 minutes ago \| parent [-]
		Doesn't this entirely depend on the latent embeddings of strategies and game space in the AI model, which may not be so concrete and explicit as you've described? That's kind of the magic of LLMs with coding, they can generalize because the abstract patterns are encoded in latent space, not the specifics.

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2 hours ago | parent | prev [-]

[deleted]

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xpct an hour ago | parent | prev | next [-]

You can definitely try to regularize against ruleset changes by generating a bunch of cards and making the agent play in randomized subsets of those cards.

I didn't look for prior work on this, but my estimate is that it's probably within 2-3 orders of magnitude of additional training compared to a static game. (Still a lot!)

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hnedeotes an hour ago | parent [-]

But wouldn't (couldn't) the model then hallucinate play patterns and get itself into problems when playing against a real opponent?

	▲	xpct 32 minutes ago \| parent [-]
		Well, if your training includes regularization against ruleset changes, the model should simply handle it. (that would be the expensive option, and require vastly more training) When the Dota 2 bot was made, they retrained the bot only partially when new patches came in, so it was definitely cheaper to adapt.

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qsort 3 hours ago | parent | prev | next [-]

There are very few missing pieces for a game like MTG. The main reasons we don't have a Stockfish for MTG is that it's a PITA to implement the rules and that nobody cares (or at least not enough to make it happen.)

There is nothing that, in principle, makes MTG different from poker or bridge, and we have superhuman engines for both.

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wavemode 2 hours ago | parent | next [-]

> There is nothing that, in principle, makes MTG different from poker or bridge

There is - metagame. There is no universal optimal strategy in a trading card game, because what is optimal depends on what decks and strategies other people are playing.

I'm sure you could train a neural network to play a specific deck within a specific metagame of a specific card game, but you would probably have to keep re-training it when there are new decks/combos/releases/rotations/banlists/metagame shifts.

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hnedeotes 2 hours ago | parent | prev | next [-]

MTG is also severely constrained (small hand, mana -> possible moves) although I don't think it's anywhere near the same. In my opinion the rules are effectively what change the whole dynamics. You can't plan as efficiently without knowing what your opponent holds and having to take into account all possibilities (with infinite energy/compute time perhaps)... I don't doubt you can train a model to play well, I just think it should be much more level to the human player. In MtG you also have the randomness which is not easy to model nor account for - the perfect play by an LLM can be the worse once the opponet draws next.

In my own game you don't have shuffle/draw randomness but the pool of options is statistically tending to infinite (if I would have 500 or 1000 scrolls designed and MtG depending on the format has that depth) when compared to something like chess, or this game. On the other hand in my own game you have to account for much more depth on the possible options your opponent has.

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dragontamer 2 hours ago | parent [-]

There's only so many card interactions that strong players actually think about.

Ex: you don't really care if the opponent plays Giant Growth or Chastise. The effect is that the opponent is playing a combat trick, and combat has moved from attackers favor into defenders favor.

To defeat an instant speed combat trick requires a combat trick of your own, or a generic counter spell of some kind. Some have interactions (ex: Doom Blade beats Giant Growth but not Chastise), but the overall gist is that opponents can do things after combat is declared. You only need to keep track of how many combat tricks you think the opponent has.

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Other situations are card advantage (ex: 2 for 1. If the opponent spends 1 cards to defeat only 2 cards of yours). The traditional card for this is Mindrot, but well placed counterspell can turn a combat trick into. 2-for-1 reversal.

You don't necessarily keep track of how your opponent makes 2-for-1 opportunities. You just have vague gists of them.

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Good spells have huge applicability. Doom blade or Murder is high because killing opponent creatures at instant speed handles the vast majority of creature buffed combat tricks, and also serves as a way to stop enemy combos and other such tricks.

In contrast, chastise is very niche. If the opponent were playing like Swords to Plowshares (powerful white instant speed removal), it's pretty much always better than chastise.

If the opponent plays chastise instead, you take that as a win because you know they could have had a deck of better cards. But for whatever reason decided to play with weaker cards...

	▲	hnedeotes an hour ago \| parent [-]
		I agree in a way, but at the same time, and I think it's a bit more applicable to MtG due to the limit of cards you can have as possible plays at any given time (outside of combos), and I believe too that you can train a bot to be good, better than average - I doubt arena doesn't have bots - but I still think that without unbound compute/time it's a game where human players have much better odds to outsmart an AI if they're good players. MtG has for the past 10 or more years been re-hashing the same play patterns, while introducing some new mechanics on most cycles, but pretty much you have staples throughout most editions that are just variations on that - card advantage, denial, combat tricks, removal, curve and then the rarity enabled bombs/combos But even then (not saying I'm right) I think the depth of choices, effects and so on, on a format like modern, or legacy, would be very difficult for an AI to top against pros. If you add draft into the mix it gets worse for the AI in my view too. Because a good play in most situations can easily be a bad play under others. That doesn't happen in chess for instance, given enough decision depth to the algos to see the future game. In my own game I think those situations can occur much easier due to you always having your full deck available. Also, in MtG it's easy to get into table states that are either ahead/behind and then you kinda just have to protect your position (like with denial decks). Then you have the effects that you might remove a creature threat (graveyard) but then that enabling a combo you weren't expecting that needs a creature on the grave, or enabling delve cards or whatever have you. It's much less clear cut for a probabilistic model to make the optimal play at every single interaction. So the more you train the model on all the variations and possible follow ups, the more you dilute its certainty isn't it? In chess, or this game, or RTS such as starcraft, that doesn't really happen in my view.

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Marazan 32 minutes ago | parent | prev [-]

> There is nothing that, in principle, makes MTG different from poker or bridge

Only in the most general form they are games with cards and hidden information with a state space that some form of tree search can theoretically play out.

The difference is the size of the search space. In MTG the search space is unimaginably huge. It would make Go's search space look like a spec of hydrogen in the middle of the universe.

It would require completely different techniques to produce a computer good at MtG than one that is good at bridge.

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empath75 an hour ago | parent | prev [-]

Hearthstone is absolutely swarming with bots that beat humans regularly.

	▲	nkrisc an hour ago \| parent [-]
		Which humans? Many people are simply not that good at Hearthstone. Are the bots regularly achieving high legend ranks?