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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?

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    Would the downvoter care to explain? I'm happy to revise this question as appropriate. Dec 2, 2021 at 18:15
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    (I didn't downvote by any means), explainable AI is generally an idea that is restricted to (typically blackbox) AI/machine learning. Endgame tablebases are certainly not machine learning and not even really AI, but instead gametree mappings. So it's not possible to 'explain' the internal logic as it is for Stockfish (which has a positional evaluation based partly on many detailed heuristics), because there is no internal logic (except 'whatever the subsequent moves, this works'). Dec 3, 2021 at 3:13

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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.

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