[mcyc]

This is a fantastic project. Thanks for sharing!

I had a nice long conversation with two of the authors of [0] at ACL.

One thing we discussed was the reverse problem. That is, as a player, could I give commands to the model and have the engine figure the moves that would best satisfy them.

This ranges from concrete like "take the black square bishop" (there is still variability like which piece should take it or if it's even possible) to more complex positional stuff like "set up to attack the kingside."

Any thoughts on this line of research?

[0] Automated Chess Commentator Powered by Neural Chess Engine (Zang, Yu & Wan, 2019) https://arxiv.org/pdf/1909.10413.pdf

[cbutner]

SentiMATE[1] looks at one of the reverse problems in a way - training an engine on commentary data - although it's not exactly what you're talking about.

I think this line of thinking could eventually lead to automated metrics for commentary evaluation, which could in turn lead to better methods than top-k/top-p for turning a bunch of sequential logits into a sentence or paragraph - basically treat it like MCTS/PUCT also.

The problem is that if you look at high-level commentary - maybe Radjabov-MVL on https://www.chess.com/news/view/2021-champions-chess-tour-fi... (I'm not the best judge, just a quick search) - it's not often possible to predict the move starting with the comment. And if you did, you might end up with very dry metrics and reverse commentary.

But this direction has a lot of potential I think, beyond just chess, into more of an algorithmic/generational support for pure NN-based language models.

[1] https://arxiv.org/pdf/1907.08321.pdf