Fundamentals of Artificial Intelligence
Fundamentals of Artificial Intelligence by Prof. K. R. Chowdhary is published by Springer Nature. This page introduces the book and provides a guide to its 21 chapters, summarising their principal topics and linking directly to the corresponding chapter pages on Springer Nature.
The book covers foundational concepts and methods of Artificial Intelligence, from logic, knowledge representation, and search to machine learning, planning, intelligent agents, data mining, natural language processing, speech recognition, and machine vision. The chapter guide is intended to help students, faculty members, researchers, and other readers identify topics relevant to their study or research.
For the complete book, publication details, and available access options, visit the official Springer Nature book page.
Chapters and Topics
Module 1: Foundations of Artificial Intelligence
Chapter 1: Introduction to Artificial Intelligence
Introduction to Artificial Intelligence, intelligent systems, history, goals of AI, the Turing Test, symbol systems, knowledge representation, engineering applications, and future directions of Artificial Intelligence.
Module 2: Logic and Automated Reasoning
Chapter 2: Logic and Reasoning Patterns
Logical foundations of AI including propositional logic, syntax, semantics, reasoning, semantic tableaux, normal forms, resolution, and non-monotonic reasoning.
Chapter 3: First Order Predicate Logic
Predicate logic, quantifiers, clause forms, unification, resolution theorem proving, Herbrand universe, most general unifier, and knowledge representation.
Module 3: Rule-Based Systems and Logic Programming
Chapter 4: Rule-Based Reasoning
Forward and backward chaining, inference engines, rule selection, model-based reasoning, case-based reasoning, and expert systems.
Chapter 5: Logic Programming and Prolog
Logic programming, facts, rules, recursion, control strategy, Prolog interpreters, and Artificial Intelligence programming examples.
Module 4: Knowledge Representation
Chapter 6: Real-World Knowledge Representation and Reasoning
Taxonomies, ontologies, commonsense reasoning, default reasoning, action and change, situation calculus, and real-world knowledge.
Chapter 7: Networks-Based Representation
Semantic networks, conceptual graphs, frames, description logic, conceptual dependency, and scripts.
Module 5: State Space and Heuristic Search
Chapter 8: State Space Search
State-space representation, breadth-first search, depth-first search, iterative deepening, bidirectional search, and complexity analysis.
Chapter 9: Heuristic Search
Hill climbing, best-first search, A* search, simulated annealing, genetic algorithms, and heuristic optimization.
Module 6: Constraint Satisfaction and Game Playing
Chapter 10: Constraint Satisfaction Problems
Constraint graphs, backtracking, constraint propagation, generate-and-test algorithms, cryptarithmetic, and CSP applications.
Chapter 11: Adversarial Search and Game Theory
Game theory, minimax search, alpha-beta pruning, game strategies, zero-sum games, and adversarial search.
Module 7: Reasoning Under Uncertainty
Chapter 12: Reasoning in Uncertain Environments
Probability theory, Bayesian reasoning, Bayesian networks, Dempster-Shafer theory, fuzzy sets, and fuzzy inference.
Module 8: Machine Learning
Chapter 13: Machine Learning
Learning systems, supervised and unsupervised learning, decision trees, inductive learning, reinforcement learning, analogy, explanation-based learning, and applications.
Chapter 14: Statistical Machine Learning
Support Vector Machines, k-nearest neighbours, Naive Bayes classifiers, artificial neural networks, deep learning, and instance-based learning.
Module 9: Automated Planning and Intelligent Agents
Chapter 15: Automated Planning
Classical planning, STRIPS, planning graphs, partial-order planning, hierarchical planning, planning languages, and multi-agent planning.
Chapter 16: Intelligent Agents
Agent architectures, autonomous agents, mobile agents, communication languages, coordination, cooperation, and coalition formation.
Module 10: Data Mining
Chapter 17: Data Mining
Knowledge discovery from large databases, classification, clustering, association rules, sequential pattern mining, and scientific applications.
Module 11: Information Retrieval and Web Intelligence
Chapter 18: Information Retrieval
Vector space models, probabilistic retrieval, indexing, query expansion, semantic web, distributed information retrieval, and Bayesian methods.
Module 12: Natural Language Processing and Speech Recognition
Chapter 19: Natural Language Processing
Natural language understanding, grammars, parsing, information extraction, question answering, commonsense reasoning, and NLP tools.
Chapter 20: Automatic Speech Recognition
Speech recognition processes, language and acoustic models, Hidden Markov Models, Kaldi, CMU-Sphinx, HTK, and DeepSpeech.
Module 13: Computer Vision
Chapter 21: Machine Vision
Image understanding, computer vision, object recognition, 3-D reconstruction, object tracking, robot vision, and vision applications.
Access the Book
The complete text, publication details, and available access options for Fundamentals of Artificial Intelligence are available through Springer Nature.