Overview

Artificial Intelligence has evolved into one of the most transformative forces of the 21st century. While deep learning, reinforcement learning, and data-driven modeling dominate the global conversation, the true heart of intelligent systems lies in their ability to make effective decisions under uncertainty. From autonomous vehicles navigating complex roads to financial models evaluating risk, from robots operating in dynamic environments to intelligent assistants optimizing user preferences-decision-making forms the core of intelligent behavior. This book, ""Decision Theory and AI Planning: Mathematical Foundations, Algorithms, and Applications in Uncertain Environments"", authored by Anshuman Mishra, is designed as a complete, rigorous, and application-oriented guide for students, researchers, academicians, AI practitioners, data scientists, machine learning engineers, and professionals working in intelligent systems. It fills a critical gap in the current literature by bringing together decision theory, utility models, probabilistic reasoning, sequential decision systems, planning algorithms, Markov processes, reinforcement learning foundations, and real-world AI applications under one unified framework. While each of these domains is vast on its own, their true power is realized only when they converge-and that convergence is exactly what this book delivers. This description outlines the purpose, design philosophy, unique strengths, and academic value of this comprehensive reference work in more than 3000 words, ensuring clarity about what this book offers and how it supports the learning and professional development of readers across disciplines. 1. Purpose and Vision of This Book The primary purpose of this book is to simplify and democratize the complex mathematical world of AI-based decision-making for learners at different levels. Decision theory is traditionally taught through abstract mathematics, while AI planning is often taught through algorithms and models. These two areas rarely appear together in an integrated, applied form. This book bridges this divide. The vision is simple: To equip readers with the theoretical foundations and practical tools needed to build intelligent agents capable of making optimal decisions in uncertain environments. Where many books focus on either pure mathematics or purely algorithmic perspectives, this work combines: Mathematical foundations Utility functions and rational choice Probabilistic modeling Sequential decisions Markov Decision Processes Planning algorithms Reinforcement learning connections Multi-agent interactions Application-driven illustration and case studies With this integrated approach, readers not only learn how decisions are made but also why certain decisions are rational, optimal, or robust in uncertain and dynamic contexts. 2. What Makes This Book Unique This book stands out for several key reasons: 2.1 Holistic Integration of Theory and Planning Most AI books cover planning and decision theory separately. This book merges them into a single narrative that treats decision theory as the mathematical backbone of AI planning. 2.2 Uncertainty-Centric View Modern AI applications require handling uncertainty. This book emphasizes: Unknown outcomes Dynamic environments Partial observability Risk and reward optimization Probability-driven decision models This makes it extremely relevant for cutting-edge AI systems.

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