Chess, Artificial Intelligence, and Epistemic Opacity

Grünke Paul

Absztrakt / abstract

In 2017 AlphaZero, a neural network-based chess engine shook the chess world by convincingly beating Stockfish, the highest rated chess engine. In this paper, I describe the technical differences between the two chess engines and based on that discuss the impact of the modeling choices on the respective epistemic opacities. I argue that the success of AlphaZero’s approach with neural networks and reinforcement learning is counterbalanced by an increase in epistemic opacity of the resulting model.


Opacity; Machine Learning; Modeling; Chess

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