Thread

1. hnedeotes · 1790969271 · focus · HN ↗

   I think that what makes these games beatable repeatedly is that they&#x27;re static. Not saying an algorithm properly trained won&#x27;t play better than the average player a game like MtG, or my own <a href="https:&#x2F;&#x2F;aethersummon.com" rel="nofollow">https:&#x2F;&#x2F;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 &quot;cards&quot;, 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 &quot;card&quot; 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.

   1. qsort · 1790970847 · focus · HN ↗

      There are very few missing pieces for a game like MTG. The main reasons we don&#x27;t have a Stockfish for MTG is that it&#x27;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.

      1. askjdfksdbfhk · 1790986651 · focus · HN ↗

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

         We don&#x27;t have superhuman play for bridge.

         Poker and bridge are quite different from each other in terms of solving them. Among other things, the hidden information space in poker (at least, in hold&#x27;em) is far smaller than in bridge (or Stratego, for that matter, as discussed in the linked paper). This makes hold&#x27;em solvable using CFR, an algorithm which essentially optimizes play by considering all the possible holdings than the opponent might have and their best strategy with each one. Even going from two to four hidden cards per player (Omaha) requires a slightly different approach although you can still use CFR as the basis for the search algorithm.

         Bridge has 13 hidden cards per player which makes CFR basically impossible to apply, at least in any obvious way--just way too many states. Similarly you see it&#x27;s not used at all in this Stratego paper.

         1. qsort · 1791009690 · focus · HN ↗

            Sure, but you can do Monte Carlo with a double-dummy solver.

            The point is that, especially for games perceived as being lower-status like MTG and other board games, I&#x27;m more inclined to believe the answer is closer to &quot;nobody is willing to pour in the resources to seriously try&quot; as opposed to &quot;we definitively cannot with current science and technology.&quot;

            1. askjdfksdbfhk · 1791052332 · focus · HN ↗

               Monte Carlo with a double-dummy solver is fundamentally insufficient for good single-dummy play because it is incapable of understanding information. It won&#x27;t take discovery plays (lines aimed at discovering more information about the opponents&#x27; hands before choosing a line of play) and will systemically overvalue positions which are good double dummy but require a guess. It doesn&#x27;t understand falsecarding (because double dummy, it doesn&#x27;t matter).

               I agree that many games could make progress if people were actually inclined to try.

               I think it will get a bit better in the coming decade thanks to continued hardware improvements &amp; powerful LLM coding agents making it more feasible for amateurs to tackle these things at home. Personally I&#x27;ve been working on a game AI project for the last month at home based around published techniques for a similar game, using my 5090 for training and Opus for implementation and orchestrating tasks and so on. It&#x27;s going quite well and it looks like I&#x27;m on track for a SOTA, superhuman AI at the end. Doing this ten years ago would have been incomparably harder.

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