The researchers pruned out commentary that did not relate to high-quality moves and examples that were too ambiguous, he added. "Then they used a special type of recurrent neural network and word embeddings (a mathematical technique that connects words on the basis their meanings), trained on another state-of-the-art model for analyzing language."
The algorithm, called SentiMATE, worked out by itself the basic rules of chess as well as several key strategies—including forking and castling.
The team found that SentiMATE was capable of evaluating chess moves "based on a pre-trained sentiment evaluation function." They concluded that there was strong evidence to support natural language processing being used for training an evaluation function in chess engines.
The performance of their solution was less than spectacular. Knight said, "it failed to beat some conventional chess bots consistently." That, however, should not distract from the fact that SentiMATE worked, and the manner in which it worked:
"SentiMATE surprised the researchers with its ability to work out some of the basic tenets of chess as well as several key strategies, such as forking (when two or more pieces are simultaneously threatened) and castling (when the king and castle both move to a more defensive position on the back of the board," the authors said.
