Description
Decision Theory and AI Planning
Advanced Decision Models, Markov Processes, Reinforcement Learning, and Intelligent Planning Systems (Vol. 2)
Artificial Intelligence is fundamentally a science of intelligent decision-making. As AI systems become increasingly autonomous, they must reason under uncertainty, optimize long-term rewards, interact with multiple agents, and adapt to dynamic environments. These capabilities rely on sophisticated mathematical frameworks such as Markov Decision Processes (MDPs), Bayesian Decision Theory, Reinforcement Learning, Game Theory, and Sequential Planning.
Decision Theory and AI Planning: Advanced Decision Models, Markov Processes, Reinforcement Learning, and Intelligent Planning Systems (Vol. 2) extends the concepts introduced in Volume 1 and explores the advanced mathematical foundations that power modern intelligent systems.
This volume provides a comprehensive treatment of Markov Models, MDPs, POMDPs, Bellman Equations, Value Iteration, Policy Iteration, Bayesian Networks, Multi-Agent Decision Making, Reinforcement Learning, Robotics Planning, Autonomous Vehicles, Healthcare Decision Systems, and Business Analytics.
Readers will learn how AI systems make optimal decisions despite uncertainty, incomplete information, noisy environments, and complex sequential interactions. Practical examples and real-world applications demonstrate how these mathematical models are used in robotics, self-driving vehicles, healthcare diagnostics, financial planning, industrial automation, and intelligent business systems.
Designed for students, researchers, AI engineers, and industry professionals, this book combines theoretical foundations with practical AI applications, making it an essential resource for advanced studies in Artificial Intelligence and intelligent planning.
What You’ll Learn
✔ Markov Processes
✔ State Transition Models
✔ Reward Functions
✔ Markov Decision Processes (MDPs)
✔ Bellman Equations
✔ Value Iteration
✔ Policy Iteration
✔ Optimal Policy Computation
✔ Sequential Decision Making
✔ Partially Observable Markov Decision Processes (POMDPs)
✔ Belief State Representation
✔ POMDP Algorithms
✔ Bayesian Decision Theory
✔ Bayesian Optimal Decisions
✔ Bayesian Networks
✔ Maximum Likelihood Estimation
✔ Maximum A Posteriori (MAP)
✔ Bayes Classifiers
✔ Decision Making Under Sparse Data
✔ Game Theory
✔ Nash Equilibrium
✔ Cooperative Games
✔ Non-Cooperative Games
✔ Multi-Agent Systems
✔ Mixed Strategy Decisions
✔ Reinforcement Learning Fundamentals
✔ Exploration vs Exploitation
✔ Policy Gradient Methods
✔ Deep Reinforcement Learning
✔ AI Planning
✔ Robot Motion Planning
✔ Autonomous Navigation
✔ SLAM-Based Decision Systems
✔ Risk-Aware Planning
✔ Autonomous Vehicles
✔ Multi-Agent Traffic Planning
✔ Healthcare AI
✔ Medical Decision Support Systems
✔ Treatment Planning
✔ Portfolio Optimization
✔ Financial Risk Assessment
✔ Business Decision Analytics
✔ Predictive Planning
✔ Intelligent Decision Support Systems
Table of Contents
Part V – Markov Decision Processes (MDP)
Chapter 13
Markov Models
Chapter 14
Markov Decision Processes
Chapter 15
Partially Observable Markov Decision Processes (POMDPs)
Part VI – Advanced Decision Models
Chapter 16
Game Theory and Multi-Agent Decision Making
Chapter 17
Bayesian Decision Theory
Chapter 18
Reinforcement Learning in Decision Making
Part VII – AI Planning Systems & Applications
Chapter 19
Robotics Planning
Chapter 20
Planning in Autonomous Vehicles
Chapter 21
Decision Theory in Healthcare
Chapter 22
Business & Finance Decision Systems
Who Should Read This Book?
This book is ideal for:
- B.Tech Students
- BCA Students
- MCA Students
- M.Tech Students
- Computer Science Students
- Artificial Intelligence Students
- Machine Learning Engineers
- Reinforcement Learning Researchers
- Robotics Engineers
- Autonomous Vehicle Developers
- Data Scientists
- Business Analytics Professionals
- Healthcare AI Researchers
- Software Engineers
- University Faculty
- Research Scholars
- Decision Scientists
- Operations Research Professionals
- Competitive Examination Aspirants
Key Features
✅ Comprehensive coverage of advanced Decision Theory
✅ Markov Decision Processes (MDPs) explained step by step
✅ Practical understanding of POMDPs
✅ Bellman Equations and Policy Optimization
✅ Bayesian Decision Theory and Bayesian Networks
✅ Reinforcement Learning with AI Planning
✅ Multi-Agent Decision Making and Game Theory
✅ Robotics and Autonomous Vehicle Planning
✅ Healthcare and Financial Decision Systems
✅ Real-world case studies and AI applications
✅ Research-oriented and industry-relevant content
✅ Suitable for university courses, projects, research, and self-learning
Why This Book?
Modern Artificial Intelligence systems operate in uncertain and dynamic environments where optimal decision-making requires more than simple algorithms. This book presents an integrated framework combining Decision Theory, Markov Models, Reinforcement Learning, Bayesian Inference, Game Theory, and Intelligent Planning to solve real-world AI problems.
Unlike traditional books that discuss these topics independently, this volume demonstrates how they work together in intelligent autonomous systems. From robot navigation and autonomous driving to healthcare diagnostics, financial planning, and business analytics, readers gain both mathematical insight and practical knowledge required for designing next-generation AI systems.
Whether you are pursuing higher education, conducting AI research, or developing intelligent applications, this book provides a strong theoretical and practical foundation for advanced decision-making under uncertainty.
Book Details
Title: Decision Theory and AI Planning
Subtitle: Advanced Decision Models, Markov Processes, Reinforcement Learning, and Intelligent Planning Systems
Volume: Vol. 2
Author: Anshuman Mishra
Publisher: Anshuman Mishra
Publication Year: 2025
Language: English
Category: Artificial Intelligence, Decision Theory, Reinforcement Learning, Machine Learning, Robotics, Autonomous Systems, Applied Mathematics
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