Before data-driven deep learning took over, Expert Systems were the pinnacle of commercial AI. The book details how these systems mimic human specialists:
4. Handling Uncertainty: Fuzzy Logic and Probabilistic Reasoning
The book is divided into 12 chapters, covering a wide range of topics in AI and Intelligent Systems. The chapters are organized into three parts:
Suitable for both beginners (undergraduates) and advanced researchers (postgraduates) due to its inclusive range of topics. mentioned in these chapters, such as Genetic Algorithms State Space Search
In conclusion, "Artificial Intelligence and Intelligent Systems" by N.P. Padhy is a comprehensive textbook that provides a clear and concise introduction to the subject. The book covers a wide range of topics, including machine learning, neural networks, fuzzy logic, and expert systems. With its clear explanations, examples, and case studies, the book is an excellent resource for students and professionals interested in AI and intelligent systems.
The book is highly structured, moving from fundamental search algorithms to complex paradigms like expert systems, fuzzy logic, and genetic algorithms. Each chapter balances theoretical frameworks with pseudo-code, real-world examples, and exercises to reinforce learning. Core Themes and Chapter Breakdown 1. Introduction to AI and Search Techniques
What sets this textbook apart from other literature in the field is its student-centric design: