
Phoenix: A Self-Optimizing Chess Engine by IBM Research India - laudney
http://arxiv.org/abs/1603.09051
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laudney
"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. However there are still many areas
in which humans excel in comparison with the machines. One such area is chess.
Even with great advances in the speed and computational power of modern
machines, Grandmasters often beat the best chess programs in the world with
relative ease. This may be due to the fact that a game of chess cannot be won
by pure calculation. There is more to the goodness of a chess position than
some numerical value which apparently can be seen only by the human brain.
Here an effort has been made to improve current chess engines by letting
themselves evolve over a period of time. Firstly, the problem of learning is
reduced into an optimization problem by defining Position Evaluation in terms
of Positional Value Tables (PVTs). Next, the PVTs are optimized using Multi-
Niche Crowding which successfully identifies the optima in a multimodal
function, thereby arriving at distinctly different solutions which are close
to the global optimum."

