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These are some pretty wild claims. How are you able to define what optimal play in chess is in order for you to say that "programs are nowhere near optimal play"?? Are you a chess grand master?
You don't need to be one. There are thousands of computer matches every day and top programs regularly score assymetrically from various starting positions (say 1-0 with white and 1/2 with black). That alone is enough to make the claim but there are different ways as well.
You may want to visit talkchess forum which is great resource with many very smart chess programmers talking about this stuff on regular basis.
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is imperfect recall the academic way of saying claudico doesn't have a full strategy stored and calculates its strategy on the fly?
More or less but it doesn't matter for the topic at hand. Poker is a perfect recall game and w/e techniques you use internally won't change it. Your result can still be assessed by general techniques.
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punter never really addressed this and instead asked what's the value in creating a strategy without being able to assess how far from equilibrium it is,
There is value but you can't claim anything about being close to equilibrium or using equilibrium approximating algorithms. If you make a strategy and say: "guys this is awesome I think it wins vs regs online!" then claim that and not equilibrium approximation. You either measure it or you don't make claims about it.
It's tilting to me because from my perspective (and from fellow programmers better than me at this) it's just very basic stuff. I thought it's some kind of misunderstanding when I've seen the professor talking about it at first but now I see it isn't. Good for poker I guess but really scary that with that kind of computing power you can get as strong entity despite all this.
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for instance, your algorithm generates successive strategies such that strategy n beats all strategies 1 to n-1, then you're still getting closer to equilibrium with each successive iteration without ever knowing how far you are from it, and inevitably you will eventually arrive at equilibrium with enough iterations.
Yes but the very fact that you are doing it means you can measure it and measuring is crucial for tweaks/experiments/compmaring algorithms.
If you don't measure you are betting that your wild guess about what's efficient will work. It's just very wrong way to go about it even if you have thousands of cores. There are so many variations of those algorithms and so many ways to improve them. By not having a measure you are walking blind.
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that doesn't mean there still isn't immense value in getting close to equilibrium, or at the very least demonstrably improving.
"Demonstrabtly" = you measure up or you shut up.
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we are from a complete understanding of the laws of physics, so what value is there in even trying to understand them in the first place?
It's bad analogy. It's an optimization problem. In optimization problems you measure your results. Say you are developing algorithms to approximate solution in Travelling Salesman Problem or w/e other difficult problem. The way you compare those algorithms is by measuring the quality of the solution.
It's poker. It's simple, well defined, relatively small card game. Let's not get carried away with law of physics or what not.
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The best algorithms for doing this create abstract games that have imperfect recall. For example, we use no abstraction for preflop and can differentiate between K2o and K3o. But on a KQT flop, we might "bucket" K2o and K3o together, so that we can't differentiate between them and are forced to play identically with them. This seems pretty reasonable, as they're pretty similar hands on this board. But technically this now has imperfect recall, as we can't distinguish between hands we previously could.
How do you know those algorithms are the best if you don't measure them up at what they are supposed to be doing?
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Nash equilibria are theoretically not guaranteed to exist in games with imperfect recall (even though they exist for full NLHE which has perfect recall), and there are no theoretical guarantees on our equilibrium-finding algorithm for them. But it does well empirically.
How do you know how it does empirically if you don't measure how well they do at what they are supposed to be doing?
