This book provides an overview of formal decision-making methods for decentralized cooperative systems. It is aimed at graduate students and researchers in artificial intelligence and related fields such as operations research and control theory. The book assumes some background knowledge, including familiarity with agents, search techniques, and probability theory. It builds upon previous works by the authors and covers a wide range of topics, including multiagent systems, uncertainty, and applications. The book introduces the Dec-POMDP framework, which is a probabilistic generalization of multiagent systems to model uncertainty in outcomes, environmental information, and communication. It discusses motivating examples such as multi-robot coordination and efficient sensor networks, highlighting the challenges of decision-making in decentralized systems. The book covers various aspects of multiagent systems, including uncertainty, decision-making under uncertainty, and applications in different domains. The Dec-POMDP framework is used to model cooperative agents in stochastic, partially observable environments. It extends single-agent POMDPs to multiple agents, allowing for joint actions and observations. The framework is applied to various domains, including the DEC-TIGER problem, multi-robot coordination, and communication network optimization. The book discusses different planning methods, including exact and approximate methods, and explores the application of Dec-POMDPs in real-world scenarios such as robotics and sensor networks. The book also addresses the challenges of decision-making in decentralized systems, including uncertainty, communication, and coordination. It discusses various approaches to modeling and solving multiagent decision problems, including game theory, teamwork theory, and operations research. The book concludes with a discussion of future research directions and the importance of Dec-POMDPs in various applications.This book provides an overview of formal decision-making methods for decentralized cooperative systems. It is aimed at graduate students and researchers in artificial intelligence and related fields such as operations research and control theory. The book assumes some background knowledge, including familiarity with agents, search techniques, and probability theory. It builds upon previous works by the authors and covers a wide range of topics, including multiagent systems, uncertainty, and applications. The book introduces the Dec-POMDP framework, which is a probabilistic generalization of multiagent systems to model uncertainty in outcomes, environmental information, and communication. It discusses motivating examples such as multi-robot coordination and efficient sensor networks, highlighting the challenges of decision-making in decentralized systems. The book covers various aspects of multiagent systems, including uncertainty, decision-making under uncertainty, and applications in different domains. The Dec-POMDP framework is used to model cooperative agents in stochastic, partially observable environments. It extends single-agent POMDPs to multiple agents, allowing for joint actions and observations. The framework is applied to various domains, including the DEC-TIGER problem, multi-robot coordination, and communication network optimization. The book discusses different planning methods, including exact and approximate methods, and explores the application of Dec-POMDPs in real-world scenarios such as robotics and sensor networks. The book also addresses the challenges of decision-making in decentralized systems, including uncertainty, communication, and coordination. It discusses various approaches to modeling and solving multiagent decision problems, including game theory, teamwork theory, and operations research. The book concludes with a discussion of future research directions and the importance of Dec-POMDPs in various applications.