1st Edition
Explainable Agency in Artificial Intelligence Research and Practice
Preface
Editor Biographies
Contributors
1. From Explainable to Justified Agency
PAT LANGLEY
2. A Survey of Global Explanations in Reinforcement Learning
YOTAM AMITAI AND OFRA AMIR
3. Integrated Knowledge-Based Reasoning and Data-Driven Learning for Explainable Agency in Robotics
MOHAN SRIDHARAN
4. Explanation as Question Answering Based on User Guides
ASHOK GOEL, VRINDA NANDAN, ERIC GREGORI, SUNGEUN AN, AND SPENCER RUGABER
5. Interpretable Multi-Agent Reinforcement Learning with Decision-Tree Policies
STEPHANIE MILANI, ZHICHENG ZHANG, NICHOLAY TOPIN, ZHEYUAN RYAN SHI, CHARLES KAMHOUA, EVANGELOS E. PAPALEXAKIS, AND FEI FANG
6. Towards the Automatic Synthesis of Interpretable Chess Tactics
ABHIJEET KRISHNAN AND CHRIS MARTENS
7. The Need for Empirical Evaluation of Explanation Quality
NICHOLAS HALLIWELL, FABIEN GANDON, FREDDY LECUE, AND SERENA VILLATA
Index
Biography
Dr. Silvia Tulli is an Assistant Professor at Sorbonne University. She received her Marie Curie ITN research fellowship and completed her Ph.D. at Instituto Superior Técnico. Her research interests lie at the intersection of explainable AI, interactive machine learning, and reinforcement learning.
Dr. David W. Aha (UC Irvine, 1990) serves as the Director of the AI Center at the Naval Research Laboratory in Washington, DC. His research interests include goal reasoning agents, deliberative autonomy, case-based reasoning, explainable AI, machine learning (ML), reproducible studies, and related topics.
