Piosolver, the first public solver and the one mentioned in the article, has this feature.
However, what often happens is if you lock one node, then several other nodes in the game tree over-adjust in drastic ways, forcing you to lock all of the, which may be infeasiable. As a result, Piosolver recently introduced "incentives", which gives a player in the game an additional incentive to take a certain action . For example, you may suspect your opponent calls too much and doesn't raise enough, so you can just set that incentive and it will include that in its math equations and give you something similar to an exploitative solution with a much simpler UX.
This feature was literally just introduced a few months ago so it's still very much an active area of research, both for game theory nerds, and people trying to use the game theory nerd research to make money !
I still haven't seen an AI for a turn based strategy game. There's AlphaStar, but it wins via APM, not strategy.
I really don't know anything about poker AIs but could it be you are referring to Libratus and/or Pluribus[0]?
on top of this, you could think about augmenting these systems to exploit weaknesses in opponent strategies. there is some work on this, but I don't think it's done much. The famous systems that played against professionals don't use it, they just try to get as close to GTO as possible and wait for opponents to screw up.
But it sounds like that must have been either misunderstanding or some other part of the bot's algorithm I guess.
Whenever I search this stuff I get practical poker strategy guides, but none of them seem to define the term haha
Common expression is "deviate from GTO" where you know what the solver would do but decide to play differently.
Life is a lot more complicated in multiplayer poker. There are Nash equilibria, but potentially many with different payoffs, and you can't force your opponents to choose the one you're aiming for. So in that case, it's not so obvious what "optimal" means.
As for CFR adapting to opponent play: CFR could bias its compute resources towards really finely optimizing strategies for the most likely scenarios facing certain players, and it seems like this has been done during poker tournaments.
But within those situations, it would still be trying to more perfectly approximate the Nash strategy, vs. more experimental approaches which actually choose a different strategy to exploit opponent weaknesses.
The point of the optimal strategy is that it's unexploitable so you can disregard the other player's actions (in the game or outside it) entirely.
All exploitative strategies are in turn exploitable.
Whether the assumptions of the Nash equilibrium (or any of the others) make sense for your situation in a game of poker is an empirical question, right? It's not a given that playing a NE means you'll be "perfect" in the human sense of the word, or that you'll get the best possible outcome.
The best superhuman poker AIs at the moment do not play equilibriums either, for instance.
However the situation with an AI powered competitor which uses exploitative play is identical to a human, the GTO play will gradually take their chips at no risk.
It's not that they're optimal but that they've chosen not to be optimal and so that's why they lose money against GTO.
The AI is at least unemotional about this, humans with a "system" easily get tilted by GTO play and throw tantrums. How can it get there with KToff? What kind of idiot bluffs here with no clubs? Well the answer will usually be the one that's taking all your chips, be better. Humans used to seeing exploitable patterns in the play of other humans may mistake ordinary noise in the game for exploitable play in a GTO strategy and then get really angry when it's a mirage.
When there are more players, there can be multiple Nash equilibria, and (unlike the two player case) combinations of equilibrium strategies may no longer be an equilibrium strategy. So it's no longer true that you cannot be exploited, because that depends on other player's strategies too, and you cannot control those.
(See this paper for instance: https://webdocs.cs.ualberta.ca/~games/poker/publications/AAM...)
Human pressure yes. Imperfect information no.
When we talk about Nash equilibrium for a game like poker, it's already based on imperfect information.
Any exploitative AI also needs the ability to adjust in real time to a different exploitative strategy, which also needs to be not easily predictable, etc.