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Found 3 papers, 3 papers with code
Information based explanation methods for deep learning agents -- with applications on large open-source chess models
With large chess-playing neural network models like AlphaZero contesting the state of the art within the world of computerised chess, two challenges present themselves: The question of how to explain the domain knowledge internalised by such models, and the problem that such models are not made openly available.
Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network models
Neural network models are widely used in a variety of domains, often as black-box solutions, since they are not directly interpretable for humans.
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts
Self-trained autonomous agents developed using machine learning are showing great promise in a variety of control settings, perhaps most remarkably in applications involving autonomous vehicles.
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