There's an ongoing trend towards building 'Explainable' AI so that not just the results of a decision process are generated, but also some form of human-readable explanation for it. The Wikipedia article mentions that some work has been done on building explanations for decision trees, and I was able to find DecodeChess (link not given since it's a commercial entity, but it's surely not hard to find) which purports to be using XAI for positional analysis, but a little digging hasn't turned up any mention of the ideas being turned towards endgame tables in hopes of finding something more like an algorithm for the complicated endgames (e.g. KRB vs. KNN) where many of the moves seem almost entirely random. Is anyone aware of research done in this direction?
In short: yes. Although I don't have an overlook on current research. But already with the endgame KR/K, which is as much the drosophila of tablebases as chess is for KI, was handled this way, and for a CS project I wrote a whole miniarticle about exactly KR/K. Just the main relevant reference: look for Herbeck/Barth. (Paid access, any university access works though.) Honk if you can't get it or like more references/my article.