This article compares decision-making processes in chess between AlphaZero and human experts, highlighting implications for human expertise development.
Research on expertise has identified pattern recognition and selective search as keycomponents in decision making. This article compares the processes of human experts inchess with those of DeepMind’s AlphaZero (AZ), an AI program that has achievedsuperhuman performance in chess, shogi, and Go by learning through self-play, knowingonly the rules of the games. Like humans, AZ predominantly relies on pattern recognitionand selective search, raising the possibility that it exhibits intuition. However, at a microlevel, there are notable differences. In addition to being much stronger than even topgrandmasters in these games, AZ conducts more extensive look-ahead search. Moreover,AZ does not utilize several types of knowledge (e.g., episodic and semantic knowledge)that human experts draw upon. Despite these differences, AZ research has significantimplications for the psychology of human expertise, including enhancing human decision-making,developing better training methods, and potentially transforming the way expertsunderstand their domain.Keywords:
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Waters et al. (2025) studied this question.
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