Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.
Procedural Content Generation via Machine Learning (PCGML)
University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校）；Drexel University（德雷塞尔大学）；Georgia Institute of Technology（佐治亚理工学院）；Duck and Cover Games ApS；Northeastern University（东北大学）；New York University（纽约大学）
Procedural Content Generation via Knowledge Transformation (PCG-KT)
Khoury College of Computer Sciences, Northeastern University（东北大学库利计算机学院）；Department of Computing Science, Alberta Machine Learning Institute, University of Alberta（阿尔伯塔大学阿尔伯塔机器学习研究所计算科学系）；College of Science, California State Polytechnic University, Pomona（加州州立理工大学波莫纳分校理学院）；Northeastern University（东北大学）；Department of Computer Science, Worcester Polytechnic Institute（伍斯特理工学院计算机科学系）
Learning Explainable Representations of Complex Game-playing Strategies
North Carolina State University（北卡罗来纳州立大学）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Tata Research Development and Design Centre（塔塔研究开发与设计中心）；Northeastern University（东北大学）
The Distributional Uncertainty of the SHAP score in Explainable Machine Learning
Instituto de Ciencias de la Computación, UBA-CONICET（阿根廷计算机科学与技术研究所（布宜诺斯艾利斯大学-阿根廷国家科学与技术研究理事会联合机构））；Department of Computer Science, Universidad de Buenos Aires（布宜诺斯艾利斯大学计算机科学系）；Carleton Univ.（卡尔顿大学）；IMFD（智利数学物理研究所）；Universidad San Sebastián（圣塞瓦斯蒂安大学）；Department of Computer Science, University of Sheffield（谢菲尔德大学计算机科学系）；Artificial Intelligence Research Institute (IIIA-CSIC)（西班牙国家研究委员会人工智能研究所）；Universidad Católica de Chile（智利天主教大学）；CENIA Chile（智利国家环境技术中心）
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Warsaw（华沙大学）；Warsaw University of Technology（华沙理工大学）；Bielefeld University（比勒费尔德大学）；CITEC（认知交互技术中心）；Paderborn University（帕德博恩大学）
