Phoenix: A Self-Optimizing Chess Engine
This addresses the issue of high computational cost in deep learning-based chess engines for AI researchers, though it is incremental as it applies an existing method to a known bottleneck.
The paper tackles the problem of reducing the parameter count in chess-playing AI by using genetic algorithms to optimize positional value tables, achieving International Master level with only a handful of parameters after 1000 generations.
Since the advent of computers, many tasks which required humans to spend a lot of time and energy have been trivialized by the computers' ability to perform repetitive tasks extremely quickly. Playing chess is one such task. It was one of the first games which was `solved' using AI. With the advent of deep learning, chess playing agents can surpass human ability with relative ease. However algorithms using deep learning must learn millions of parameters. This work looks at the game of chess through the lens of genetic algorithms. We train a genetic player from scratch using only a handful of learnable parameters. We use Multi-Niche Crowding to optimize positional Value Tables (PVTs) which are used extensively in chess engines to evaluate the goodness of a position. With a very simple setup and after only 1000 generations of evolution, the player reaches the level of an International Master.