Chess and explainable AI

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Chess, once famously referred to as the drosophila of artificial intelligence (AI) research, has been a significant domain for developing intelligent AI agents capable of achieving super-human performance in domains previously dominated by humans. However, the emphasis on unceasingly improved playing strength has come at the cost of neglecting other fundamental aspects of intelligent agents, such as being capable of explaining the rationality behind their decisions in human-understandable terms. The need for such capabilities may be even more profound now than before, partly because such agents may be capable of learning novel concepts of interest to us humans, for example, as recently demonstrated in the game of chess. In this paper, we survey the state of explainable AI in chess-playing agents, arguing that chess may indeed hold a promise as an admissible domain for explainable AI.