A little bit ago I wrote about the Reconnaissance Blind Chess tournament and how my bot worked.
What you might notice is that for this game of RBC I used a chess engine, and had to have code around it to make up for the fact that RBC isn't really straight chess. Because of that there are some notable flaws:
If you were to try to write a tree search which took into account the sets of potential boards you would run into another problem. The imperfect information version of chess has an exponentially larger tree than regular chess, which is already a pretty massive game.
In order to even approach searching that tree you would need some way of accelerating your tree search.
To do this I developed a small algorithm which in my very preliminary testing gives around a 3 times speedup with logarithmic time and poly-logarithmic space complexity
Reconnaissance Blind Chess
Last summer I saw a posting about a competition called Reconnaissance Blind Chess. I have always been really fascinated by games so I decided to throw my hat in the ring.
A few weeks ago, with the competition all done, I was invited to NeurIPS by the JHUAPL to talk about how my bot worked.
I figured that it might be a bit interesting to other people then, to hear how my bot worked.
Hi, I'm Billy, a PhD student studying Math.