Introduction to Artificial Intelligence

This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website.

Table of Contents

Chapter 1: Introduction
Chapter 2: Propositional Logic
Chapter 3: First-order Predicate Logic
Chapter 4: Limitations of Logic
Chapter 5: Logic Programming with PROLOG
Chapter 6: Search, Games and Problem Solving
Chapter 7: Reasoning with Uncertainty
Chapter 8: Machine Learning and Data Mining
Chapter 9: Neural Networks
Chapter 10: Reinforcement Learning
Chapter 11: Solutions for the Exercises