Anaconda defeats Hoyle 6-0: a case study competing an evolved checkers program against commercially available software

We have been exploring the potential for a coevolutionary process to learn how to play checkers without relying on the usual inclusion of human expertise in the form of features that are believed to be important to playing well. In particular, we have focused on the use of a population of neural networks, where each network serves as an evaluation function to describe the quality of the current board position. After only a little more than 800 generations, the evolutionary process has generated a neural network that can play checkers at the expert level as designated by the US Chess Federation rating system. The current effort reports on a competition between the best-evolved neural network, named “Anaconda,” and commercially available software. In a series of six games, Anaconda scored a perfect six wins