A Artificial Intelligence continues to redefine the way we live, work, and think, and the need for a strong conceptual foundation in this field has never been greater. Addressing this demand, Artificial Intelligence Essentials: Principles, Search, and Reasoning emerges as a comprehensive and academically rich textbook designed to guide learners through the core principles and practical applications of AI.
This book offers a structured journey into the evolution, history, and foundational concepts of Artificial Intelligence, making it an essential resource for students and educators alike. It systematically introduces readers to the concept of intelligent agents, their structures, percepts, actuators, and task environments, while clearly distinguishing between human-like intelligence and rational decision-making models. The text emphasizes clarity and depth, ensuring that readers not only understand theoretical frameworks but also learn how to apply them effectively.
A key strength of the book lies in its detailed exploration of problem-solving techniques using state-space representation. It carefully explains how problems can be formulated with initial states, goal states, and operators, and demonstrates the application of both uninformed and informed search strategies such as Breadth-First Search (BFS), Depth-First Search (DFS), Iterative Deepening Search (IDS), Hill Climbing, Simulated Annealing, and Online Search. These techniques are evaluated based on completeness, optimality, and computational complexity, enabling learners to approach classical AI problems like the 8-Puzzle, Water Jug, and Tic-Tac-Toe with confidence and analytical precision.
The book further delves into the classification and functioning of intelligent agents, covering reflex, model-based, goal-based, utility-based, and learning agents. Readers gain insights into agent architectures and performance evaluation across diverse environments, building a strong understanding of how intelligent systems operate in real-world scenarios. The inclusion of tree and graph search fundamentals strengthens conceptual clarity, while practical comparisons between different methods enhance decision-making skills.
Heuristic search techniques form another critical component of the text. The authors explain the significance of admissible and consistent heuristics and provide a thorough understanding of algorithms such as Greedy Best-First Search and A*. The discussion on A*’s optimality and completeness equips learners with the knowledge required to solve complex problems like route optimization efficiently.
In addition, the book addresses advanced topics such as partial-observation search and local search methods, including Hill Climbing and Simulated Annealing, highlighting challenges like local maxima, ridges, and plateaus. Constraint Satisfaction Problems are explored in depth, with practical approaches like constraint graphs, propagation, backtracking, and forward checking applied to real-world examples such as map coloring, cryptarithmetic, and scheduling.
The section on adversarial search introduces readers to game theory concepts, including minimax algorithms and Alpha–Beta pruning, enabling them to understand strategic decision-making in competitive environments like Chess. This comprehensive coverage ensures that learners are well-equipped to tackle both theoretical and practical challenges in AI.
The authors bring a wealth of academic experience and research expertise to this work. Mrs. Jayashri M, an Assistant Professor with over 12 years of teaching experience, combines her strong background in Artificial Intelligence, Machine Learning, and Deep Learning with a passion for innovation and student development. Her academic contributions, including patents and awards, reflect her dedication to advancing technology and education.
Mrs. Saswati Behera, also an Assistant Professor, contributes significantly with her research experience in Artificial Intelligence and Machine Learning. Her involvement in international publications, conferences, and initiatives like the Smart India Hackathon highlights her active engagement with the research community and her commitment to nurturing future innovators.
Dr. Krishna Kumar P. R, a distinguished academician with more than two decades of experience, adds depth and authority to the book. His extensive research in IoT, Artificial Intelligence, Blockchain, and Smart Cities, along with his leadership in funded projects and numerous accolades, underscores his expertise and vision in shaping modern technological education.
Together, the authors have created a textbook that not only serves as a strong academic resource but also inspires learners to think critically and innovate in the field of Artificial Intelligence. Artificial Intelligence Essentials: Principles, Search, and Reasoning stands as a valuable contribution to AI education, bridging the gap between theory and application while preparing students for the challenges of an increasingly intelligent world.