#252 overall
CS238 Decision Making under Uncertainty
Stanford University
2 recommenders · 2 threads · recommended in 2 years, 2020–2021 · score 1.8
Stanford graduate course on decision making under uncertainty, taught by Mykel Kochenderfer, covering probabilistic models and planning methods for sequential decisions such as Markov decision processes and partially observable settings. Its companion course CS238/AA228 materials and the related AA229 advanced course are linked from the comments.
Commenters describe it as one of their favorite courses and point to Kochenderfer's material as the natural next step after general game playing, extending to state-of-the-art approaches.
Recommendations by year
20062026
I took CS238 (decision making under uncertainty) with the first listed author of this book (Mykel J. Kochenderfer).
It was one of my favorite courses, and it looks like this textbook is now used for that course:
https://web.stanford.edu/class/aa228/cgi-bin/wp/
I would check out Michael Genesereth's course on general game playing [1] -- I don't remember if we specifically implemented a chess engine, but it's more or less exactly what you're looking for. Once you've mastered that, I'd move on to Mykel Kochenderfer's series on decision making under uncertainty [2], which extends to more state of the art approaches to the problem (ie. MCTS + deep Q learning + better encoders, which is basically what got us AlphaGo, AlphaStar, Atari agents, etc.). Once you've got that down (or before), you should start reading whitepapers.
Genesereth and Kochenderfer also have books on the subject; Kochenderfer's is extremely good (I haven't read all of Genesereth's).
[1] http://ggp.stanford.edu/
[2] https://web.stanford.edu/class/aa228/cgi-bin/wp/ and https://web.stanford.edu/class/aa229/cgi-bin/wp/