>LLMs have allowed double-dummy solvers to move from ~0.1sec to microseconds (if you allow 99.9% accuracy)
I don't know what this is referring to. The ubiquitous dds library, which is AFAIK basically the only double dummy solver, has not seen any real improvements. Neither of those numbers look right to me, I think it's more in the range of ~10ms.
There's a crank who was claiming some magical improvements a little while ago that really just boiled down to AI psychosis (and having absolutely no understanding of what he was claiming). I hope that's not what you're referring to.
I'll concede an order of magnitude for dds, I may not have been using it optimally. The claims made by Lorand Dali for his neural net solver were a thousand times faster with a slight drop in accuracy (99.9%+ I think)
I believe this was used in his 'ben' bridge engine, https://github.com/lorserker/ben/ now maintained by ThorvaldAagaard, though I have to admit it now seems to be heavily dds focussed, so there may have been a rollback along the lines you outlined.
I'm attempting to recreate the concept myself, so should soon have an idea whether its moonshine or not,
I wasn't aware of this although this project had a substantial error rate. Flipping through the YouTube video it only predicted the correct number of tricks 70% of the time--so I'm not sure if your 99.9% is referring to a different project that I'm unable to find, or if you misremembered. I don't think anything like this was ever used in Ben; looking at the commit history, I think Ben has always used the standard dds library.
FWIW I'm pretty confident that you could beat the performance of the project I linked with a fairly straightforward transformer architecture.
Yes - you are right, 70% for a correct answer is what the talk says. Id misremembered the higher (~99%) numbers because for my purpose (simulating many random deals compatible with known cards to get probability distributions on hidden card positions and trick taking possibilities in each suit/notrumps ) a one trick error is quite acceptable. I agree a more straightforward architecture is a promising approach. Once I have generated weights in a form usable in C, I am also curious as to whether they can direct DDS's tree search as a kind of probabilistic oracle.
(There was some Ben code to use a neural net double dummy evaluator, because Ive used it, but it was removed. Ill try and find the git point it was removed.)
also the keras model ben/models/TF2models/RPDD_2024-07-08-E02.keras is still present in the current repo but not apparently used. It was trained on ten million deals where all double dummy info is known.
I don't know what this is referring to. The ubiquitous dds library, which is AFAIK basically the only double dummy solver, has not seen any real improvements. Neither of those numbers look right to me, I think it's more in the range of ~10ms.
There's a crank who was claiming some magical improvements a little while ago that really just boiled down to AI psychosis (and having absolutely no understanding of what he was claiming). I hope that's not what you're referring to.