Sale!

Decision Theory and AI Planning VOL-2

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…

Category: mathematics with ai

Tags: AI Algorithms, AI Planning, AI Textbook, Artificial Intelligence, Autonomous Navigation, Autonomous Vehicles, Bayes Classifier, Bayesian Decision Theory, Bayesian Networks, Bellman Equation, Business Analytics, Computational Intelligence, Computer Science, Data Science, Decision Making Under Uncertainty, Decision Theory, Decision Trees, Deep Reinforcement Learning, Financial Decision Systems, Game Theory, Healthcare AI, Intelligent Agents, Intelligent Planning Systems, Machine Learning, MAP Estimation, Markov Decision Process, Markov Models, Maximum Likelihood, MDP, Medical Decision Support, Multi Agent Planning, Multi Agent Systems, Nash Equilibrium, Operations Research, Policy Gradient, Policy Iteration, POMDP, Portfolio Optimization, Reinforcement Learning, Research Book, Risk Assessment, Risk Aware Planning, Robot Motion Planning, Robotics, Robotics Planning, Sequential Decision Making, SLAM, University Textbook, Utility Theory, Value Iteration

Description
Reviews (0)

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

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

Reviews

There are no reviews yet.

Be the first to review “Decision Theory and AI Planning VOL-2”