Algorithms for Decision Making

Automated decision-making systems or decision-support systems-used in applications that range from aircraft collision avoidance to breast cancer screening-must be designed to account for various sources of uncertainty while carefully balancing multiple objectives.This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them.
Suitable for advanced undergraduates entering the field and for professionals who want a one-stop resource, this volume : Bridges operations research, computer science, and control algorithms. Focuses on the fundamentals of a complex and rapidly changing field. Provides Julia code implementation, that students can run themselves for all algorithms. Features : Full color throughout with 225 illustrations.
Robust instructor support materials including sample syllabi, slides, figures, and Julia code. computer science Mykel Kochender-fer is Associate Professor at Stanford University, where he is Director of the Stanford Intelligent Systems Laboratory (SISL). He is the author of Decision Making Under Uncertainty (MIT Press).Tim Wheeler is a software engineer in the Bay Area, working on autonomy, controls, and decision-making systems.
Kochenderfer and Wheeler are coauthors of Algorithms for Optimization (MIT Press). Kyle Wray is a researcher who designs and implements the decision-making systems on real-world robots.