Pattern Recognition and Selective Search: Comparison between AlphaZero and Human Expertise

Research on expertise has identified pattern recognition and selective search as key components in decision making. This article compares the processes of human experts in chess with those of DeepMind’s AlphaZero (AZ), an AI program that has achieved superhuman performance in chess, shogi, and Go by learning through self-play, knowing only the rules of the games. Like humans, AZ predominantly relies on pattern recognition and selective search, raising the possibility that it exhibits intuition. However, at a micro level, there are notable differences. In addition to being much stronger than even top grandmasters 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 significant implications for the psychology of human expertise, including enhancing human decision-making, developing better training methods, and potentially transforming the way experts understand their domain. Keywords: