How we implemented mathematically rigorous Bayesian inference in Macondo to let BestBot reason about its opponent’s rack, and why it only barely edges out going first.

Scrabble endgames are computationally difficult to solve exhaustively. I used some chess programming techniques to greatly speed them up.

I have found that in the literature about games and AI (artificial intelligence) that Scrabble is invariably referred to as something of a “solved” game, in the same way that Chess, Go, etc are “solved” by AI —in these latter games, there exist AIs that have superhuman performance and can basically beat any person.

I’m going to log more of my progress on the apps that I wrote about in an earlier post, in an attempt to:

1. make myself more likely to work on these apps
2. write a log for me and others and drum up some excitement! ;)

Monte Carlo simulation is basically working on Macondo. I expect that since I just got it working, that I’ll discover some bugs and special cases, and there’s so much more I want to do with it, but for now I’m excited that I got it working. As a nod to Scrabble expert Ron Tiekert, who might have been the first person to invent Scrabble simulations back in the 1980s, I plugged in the rack that he used to explain this concept, AAADERW.