Description: Explainable Agency in Artificial Intelligence by Silvia Tulli, David W. Aha The book is a collection of cutting-edge research on the topic of explainable agency in artificial intelligence (XAI), including counterfactuals, fairness, human evaluations, and iterative and active communication among agents. FORMAT Paperback CONDITION Brand New Publisher Description This book focuses on a subtopic of explainable AI (XAI) called explainable agency (EA), which involves producing records of decisions made during an agents reasoning, summarizing its behavior in human-accessible terms, and providing answers to questions about specific choices and the reasons for them. We distinguish explainable agency from interpretable machine learning (IML), another branch of XAI that focuses on providing insight (typically, for an ML expert) concerning a learned model and its decisions. In contrast, explainable agency typically involves a broader set of AI-enabled techniques, systems, and stakeholders (e.g., end users), where the explanations provided by EA agents are best evaluated in the context of human subject studies.The chapters of this book explore the concept of endowing intelligent agents with explainable agency, which is crucial for agents to be trusted by humans in critical domains such as finance, self-driving vehicles, and military operations. This book presents the work of researchers from a variety of perspectives and describes challenges, recent research results, lessons learned from applications, and recommendations for future research directions in EA. The historical perspectives of explainable agency and the importance of interactivity in explainable systems are also discussed. Ultimately, this book aims to contribute to the successful partnership between humans and AI systems.Features:Contributes to the topic of explainable artificial intelligence (XAI)Focuses on the XAI subtopic of explainable agencyIncludes an introductory chapter, a survey, and five other original contributions Author 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. Table of Contents 1. From Explainable to Justified Agency, 2. A Survey of Global Explanations in Reinforcement Learning, 3. Integrated Knowledge-Based Reasoning and Data-Driven Learning for Explainable Agency in Robotics, 4. Explanation as Question Answering Based on User Guides, 5. Interpretable Multi-Agent Reinforcement Learning with Decision-Tree Policies, 6. Towards the Automatic Synthesis of Interpretable Chess Tactics, 7. The Need for Empirical Evaluation of Explanation Quality Details ISBN1032392584 Author David W. Aha Publisher Taylor & Francis Ltd Series Chapman & Hall/CRC Artificial Intelligence and Robotics Series ISBN-13 9781032392585 Format Paperback Subtitle Research and Practice Place of Publication London Country of Publication United Kingdom Edited by David W. Aha Illustrations 18 Tables, black and white; 2 Line drawings, color; 27 Line drawings, black and white; 3 Halftones, color; 4 Halftones, black and white; 5 Illustrations, color; 31 Illustrations, black and white AU Release Date 2023-12-20 NZ Release Date 2023-12-20 Alternative 9781032409139 Audience Tertiary & Higher Education ISBN-10 1032392584 Imprint CRC Press Year 2024 Publication Date 2024-01-22 UK Release Date 2024-01-22 DEWEY 006.31 Pages 150 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:159359910;
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