Full Article: [pdf] DOI: https://doi.org/10.22503/inftars.XIX.2019.4.1 Language: en Author(s):  Paul Grünke
Title: Chess, Artificial Intelligence, and Epistemic Opacity 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, I 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 the epistemic opacity of the resulting model. Keywords: Opacity, Machine Learning, Modeling, Chess
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