In the spectrum between "continuous" and "discrete", I wonder where the chess problem lies. AlphaZero learning from zero hard coded behaviors, and following the

In the spectrum between "continuous" and "discrete", I wonder where the chess problem lies. AlphaZero learning from zero hard coded behaviors, and following the description of exploring all the paths one might put it close to being continuous. The thing is, AlphaZero shines by aggressively not exploring lots of paths. So there must be some kind of abstraction it relies on to filter theses paths; and this represents some sort of local minima of the topology of the problem. I'd be very interested in knowing if some rules can emerge from this. And if it can be extracted from the AlphaZero internal model. Hopefully rules that can be interpreted by humans. It could be measured by: what percentage of play decisions can be reproduced with a rule expressed with under 150 symbols? Both for chess moves themselves, and for a path-pruning strategy. I think in the future explainable AI will be a standard part of the AI/ML stacks. The DARPA launched recently a program titled just like this [1]. They talk about deep learning; but I'd be very interested if some results can be had with Reinforcement Learning. [1] https://www.darpa.mil/program/explainable-artificial-intelligence https://www.darpa.mil/program/explainable-artificial-intelli...