Decision Theory and AI Planning

Mathematical Foundations, Algorithms, and Applications in Uncertain Environments

In today’s rapidly evolving world of Artificial Intelligence (AI), Machine Learning (ML), Robotics, Autonomous Systems, and Intelligent Decision Support Systems, the ability to make optimal decisions under uncertainty has become one of the most important challenges in computer science and engineering.

Decision Theory and AI Planning: Mathematical Foundations, Algorithms, and Applications in Uncertain Environments provides a comprehensive and application-oriented introduction to the mathematical principles that enable intelligent systems to reason, evaluate alternatives, and plan effectively in uncertain environments.

This book bridges the gap between classical decision theory, probability theory, utility theory, optimization, and modern Artificial Intelligence planning algorithms. It explains how intelligent agents evaluate risks, maximize expected utility, construct decision trees, perform Bayesian reasoning, and generate optimal action plans in dynamic and uncertain environments.

Written in a structured, beginner-friendly, and research-oriented style, this book combines mathematical foundations with practical AI applications, making it suitable for university students, researchers, software developers, AI engineers, and professionals working in intelligent systems.

What You’ll Learn

✔ Foundations of Decision Theory

✔ Rational Decision Making

✔ Intelligent Agents

✔ Utility-Based Systems

✔ Mathematical Modeling of Decisions

✔ Probability Theory

✔ Random Variables

✔ Conditional Probability

✔ Joint Probability

✔ Bayesian Reasoning

✔ Decision Making Under Uncertainty

✔ Utility Theory

✔ Preference Modeling

✔ Risk Analysis

✔ Utility Functions

✔ Linear Utility Models

✔ Nonlinear Utility Models

✔ Exponential Utility Functions

✔ Bernoulli Utility

✔ Expected Utility Theory

✔ Von Neumann–Morgenstern Utility Theory

✔ Stochastic Dominance

✔ Utility Optimization

✔ Multi-Attribute Utility Theory (MAUT)

✔ Decision Trees

✔ Influence Diagrams

✔ Sequential Decision Making

✔ Backward Induction

✔ Policy Optimization

✔ Artificial Intelligence Planning

✔ State Space Representation

✔ Action Representation

✔ Deterministic Planning

✔ Stochastic Planning

✔ STRIPS Planning

✔ GraphPlan Algorithm

✔ Partial Order Planning

✔ Heuristic Search

✔ Planning Under Uncertainty

✔ Incomplete Information

✔ Knowledge Representation

✔ Intelligent Planning Systems

✔ AI Decision Support Systems

✔ Autonomous Decision Making

✔ Optimization in Artificial Intelligence

Part I – Introduction to Decision Theory & AI Planning

Chapter 1

Foundations of Decision Theory

Chapter 2

Basic Concepts in Probability and Uncertainty

Part II – Utility Theory and Utility Functions

Chapter 3

Utility Theory Basics

Chapter 4

Utility Function Types

Chapter 5

Multi-Attribute Utility Theory (MAUT)

Chapter 6

Expected Utility Theory

Part III – Decision Trees and Sequential Decisions

Chapter 7

Decision Trees in Artificial Intelligence

Chapter 8

Influence Diagrams

Chapter 9

Sequential Decision Making

Part IV – AI Planning in Deterministic and Uncertain Environments

Chapter 10

Introduction to Artificial Intelligence Planning

Chapter 11

Classical Planning Algorithms

Chapter 12

Planning Under Uncertainty

Who Should Read This Book?

This book is ideal for:

B.Tech Students
BCA Students
MCA Students
B.Sc. Computer Science Students
M.Tech Students
Artificial Intelligence Students
Machine Learning Students
Data Science Students
Robotics Engineering Students
Software Engineers
AI Engineers
Data Analysts
Research Scholars
University Faculty
Operations Research Professionals
Business Analytics Professionals
Decision Scientists
Engineering Professionals
Competitive Examination Aspirants

Key Features

✅ Comprehensive coverage of Decision Theory and AI Planning

✅ Mathematical foundations for intelligent decision-making

✅ Step-by-step explanation of probability and uncertainty

✅ Utility theory with practical examples

✅ Decision trees and influence diagrams

✅ Sequential decision-making algorithms

✅ Classical AI planning techniques

✅ Planning under uncertainty

✅ Bayesian reasoning and stochastic optimization

✅ Real-world AI planning applications

✅ University syllabus aligned

✅ Suitable for research, projects, and self-learning

Why This Book?

Unlike traditional textbooks that treat decision theory, probability, and AI planning as separate topics, this book presents them as an integrated framework for designing intelligent systems. It demonstrates how mathematical models, utility functions, probabilistic reasoning, and planning algorithms work together to solve complex decision-making problems in uncertain environments.

Readers will gain both theoretical knowledge and practical insights into building AI systems capable of reasoning, evaluating alternatives, minimizing risk, maximizing utility, and generating intelligent action plans. The book is valuable for academic study, competitive examinations, research, and industrial AI applications.

Book Details

Title: Decision Theory and AI Planning

Subtitle: Mathematical Foundations, Algorithms, and Applications in Uncertain Environments

Series: A Complete Guide to Utility Functions, Decision Trees, and Intelligent Planning Systems

Author: Anshuman Mishra

Publisher: Anshuman Mishra

Publication Year: 2025

Language: English

Category: Artificial Intelligence, Decision Theory, AI Planning, Machine Learning, Computer Science, Applied Mathematics

Conditional Probability

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