Artificial Intelligence: Foundations of Computational Agents

This third edition textbook offers a fully revised and updated comprehensive guide to artificial intelligence, suitable for both undergraduate and graduate courses. It introduces three new chapters covering neural networks and deep learning—including generative AI—as well as causality and the social, ethical, and regulatory impacts of AI technologies. The book utilizes a novel agent design space to provide a coherent framework for understanding learning, reasoning, and decision-making processes. All sections have been updated to reflect methods proven to work in real-world scenarios, ensuring students learn current best practices. To facilitate practical understanding, every concept and algorithm is presented in both pseudocode and open-source AIPython code, allowing students to experiment with and build upon the implementations. The text integrates numerous realistic applications and examples, with five larger case studies developed throughout the book to connect design approaches directly to applications. Additionally, each chapter now includes a dedicated social impact section, helping learners grasp the broader implications of the techniques they study. The book is supported by a complete teaching package, including lecture slides, solutions, and code resources.