@inproceedings{96489ba4607a43d8a9bb8841a2cac2db,
title = "Evaluating Interpretability Methods for DNNs in Game-Playing Agents",
abstract = "There is a trend in game-playing agents to move towards an Alpha-Zero-style architecture, including using a deep neural network as a model for evaluating game positions. Model interpretability in such agents is problematic. We evaluate the applicability and effectiveness of several saliency-map-based methods for improving the interpretability of a deep neural network trained for evaluating game positions, using the game of Breakthrough as our testbed. We show that the more applicable methods provide insights into the importance of the different game pieces and other domain-dependent knowledge learned by the model.",
keywords = "deep neural-networks, game-playing, model-interpretability",
author = "A{\dh}alsteinn P{\'a}lsson and Yngvi Bj{\"o}rnsson",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 17th International Conference on Advances in Computer Games, ACG 2021 ; Conference date: 23-11-2021 Through 25-11-2021",
year = "2022",
doi = "10.1007/978-3-031-11488-5\_7",
language = "English",
isbn = "9783031114878",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "71--81",
editor = "Cameron Browne and Akihiro Kishimoto and Jonathan Schaeffer",
booktitle = "Advances in Computer Games - 17th International Conference, ACG 2021, Revised Selected Papers",
address = "Germany",
}