| ID |
263
|
| Authors |
GRÜNKE Paul
|
| Title |
Chess, Artificial Intelligence, and Epistemic Opacity
|
| Title (translation) |
|
| Subtitle |
|
| Subtitle (translation) |
|
| Keywords |
Opacity, Machine Learning, Modeling, Chess
|
| Keywords (translation) |
|
| Issue |
2019/4
|
| DOI |
https://doi.org/10.22503/inftars.XIX.2019.4.1
|
| 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.
|
| Abstract (translation) |
|
| Language |
English
|
| Pages |
7-17
|
| Column |
Tanulmányok
|