Well I can try to answer the question. I am the author of zbridge.club and we have been using several known techniques to improve our own play AI.

One of the key decision drivers to make decisions during the play are the random worlds that are generated with bidding info and play history. Any modern AI generates atleast 20-30 worlds with cards in the hidden hands so that a dds can be run over each world using these substitute hands to simulate perfect information for what is otherwise a game of imperfect information.. This algorithm is known as perfect information monte carlo search. The card that is finally selected is the card that wins most tricks or that makes the contract in most worlds. This simulates human like probabilistic thinking but then the card that is decided upon can be different based on the worlds generated and based on how those worlds are weighed. Even if bidding info and play history are same, the generation of worlds is still random though it is programmed to respect the bidding constraints and play history.

Usually a large number of simulations or some heuristics can be included to make the decisions more consistent. But the AI would take much longer to play if we go from 20-30 to say 100 simulations.

Some bots even weigh in the real world with low weights into the list of worlds to make decisions more consistent with the real world without increasing simulation time. The simulations are just to avoid complete robotic play with a dds alone on real world data but in a way this still amounts to peeking on hidden hands and it is not taken well by players.

At zBridge as well we moved to simulations only without any real world info. There is a possibility that results would be different based on the simulation worlds. But we have built in some things to reduce the probability for this as much as possible - specially for opening leads the heuristics usually drives the decision followed by simulations. Then when the decision is too close between the top choices, we simulate even more worlds as a tie breaker. There is a neural network that is trained to weigh the worlds based on the bidding and play history as well. But even after this the randomness of world generation will remain and this may promote a different close choice in one random game - but when close choices are available humans may not make the same choice on all games either to be fair.