商品描述
This book focuses on a subtopic of explainable AI (XAI) called explainable agency (EA), which involves producing records of decisions made during an agent's 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 agency
- Includes an introductory chapter, a survey, and five other original contributions
商品描述(中文翻譯)
本書專注於可解釋人工智慧（XAI）的一個子主題，稱為可解釋代理（EA），其中包括在代理人推理過程中產生決策記錄，以人類可理解的方式總結其行為，並回答有關特定選擇及其原因的問題。我們將可解釋代理與可解釋機器學習（IML）區分開來，IML是XAI的另一個分支，專注於為機器學習專家提供有關學習模型及其決策的洞察。相比之下，可解釋代理通常涉及更廣泛的AI技術、系統和利益相關者（例如最終用戶），EA代理提供的解釋最好在人類主體研究的背景下進行評估。
本書的章節探討了賦予智能代理可解釋代理能力的概念，這對於在金融、自動駕駛車輛和軍事行動等關鍵領域中獲得人類的信任至關重要。本書介紹了來自不同角度的研究人員的工作，描述了EA中的挑戰、最新研究成果、應用案例中的經驗教訓，以及對未來研究方向的建議。本書還討論了可解釋代理的歷史背景和交互性的重要性。最終，本書旨在促進人類與AI系統之間的成功合作。
特點：
- 對可解釋人工智慧（XAI）主題做出貢獻
- 專注於XAI的可解釋代理子主題
- 包括一個介紹性章節、一個調查和其他五個原創貢獻
作者簡介
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.
作者簡介(中文翻譯)
Dr. Silvia Tulli 是巴黎索邦大學的助理教授。她獲得了瑪麗居里行動研究獎學金並在里斯本高等技術學院完成了她的博士學位。她的研究興趣涉及可解釋人工智慧、互動式機器學習和強化學習的交叉領域。
Dr. David W. Aha（1990年畢業於加州大學爾灣分校）擔任華盛頓特區海軍研究實驗室人工智慧中心的主任。他的研究興趣包括目標推理代理、審慎自主性、案例推理、可解釋人工智慧、機器學習（ML）、可重複研究和相關主題。
