The purpose of this query is to discuss a topic that I am totally ignorant of, rather than pose one specific question. Although such a query does invite opinions based on intuition, I am also looking for objective facts based on people's experience. If this query is inappropriate for this website, please suggest a different forum.
I am wondering whether anyone has begun combining freeware AI with dynamically generated endgame tablebases to attack chess studies.
The following link discusses freeware chess AI:
https://en.wikipedia.org/wiki/Leela_Chess_Zero
The following link discusses chess AI in general:
A purely self-trained chess AI
The following link may relate to building a tablebase on-the-fly to attack
a specific position:
http://kirill-kryukov.com/chess/discussion-board/viewtopic.php?p=16108#p16108
The general algorithm for constructing a tablebase is discussed here:
http://www.talkchess.com/forum3/viewtopic.php?t=51847
Suppose (for example) Leela Chess Zero (with standard 6 man tablebases) was used to play N games against itself, always starting from a specific position. Assume that Leela Chess Zero (somehow) learns from each game and stores the knowledge. The (hopefully plausible) intent is that by playing these games, Leela Chess Zero acquires pertinent insights that allow it to determine "best play".
The following links suggest sample positions to attack:
LiChess puzzle 82753: can white win with a queen and two pawns vs. rook and four pawns?
Solvable studies that computers fail to crack
Are there positions which cannot be properly analyzed by any computer?
Can such an approach be implemented on a modern, moderately priced home pc?
To attack a typical position:
1. How large should N be (i.e. how many games played) to give Leela Chess Zero
a plausible chance of "learning"
insights into the position?
2. What time controls should be employed and how long will the process take?
3. How much memory (? + GPU memory) should be employed?
4. How strong a cpu (? + GPU-processor) should be employed?
Assuming that the above issues are resolved, how does one determine what the insights are? Would it be possible to identify a position that Leela Chess Zero categorizes as both obtainable and "critical"?
Assume that Leela identifies a specific (critical and obtainable) position via its FEN. Would it be possible to build a tablebase (from scratch) with the critical position as a starting point, so that only those positions that might legally result from this starting point are relevant?
Suppose that the approach described in the previous paragraph was feasible, with a clear determination reached of [win for white, win for black, or draw]. Could these results be fed back into Leela Chess Zero so that it used this result + all the learning that it had achieved on the position, to start a new series of N games (with Leela Chess Zero playing itself) from the original starting position?
Is it plausible that the iterative process of combining Leela Chess Zero with dynamically generated tablebases could reach a definitive conclusion of (for example) a complex middle game with many pieces?
