They don't have neural nets or fancy nonlinear function approximators for board state evaluation Nonlinear function approximations are pretty common due to com

They don't have neural nets or fancy nonlinear function approximators for board state evaluation Nonlinear function approximations are pretty common due to combining several evaluation heuristics and indexing in to tables with weights with the output of previous heuristics. As for neural networks - the extra nonlinear layers seem to have too much computational overhead compared to the increase in evaluation accuracy. Chess board state can be reasonably approximated with linear terms and few cherry-picked higher order terms.