Artificial intelligence (AI) aims at solving problems using computer algorithms that mimic human intelligence. This course provides a technical introduction of fundamental concepts of AI. We cover four major parts of AI: (1) Solving problems by search, constraint satisfaction, game playing; (2) Knowledge representation and reasoning using logic, expert systems; (3) Probability distributions, Bayes theorem, Naïve Bayes classification, Bayesian networks and decision graphs; (4) Natural Language Processing （NPL）.
This book explores the full breadth and depth of the field of artificial intelligence, which encompasses logic, probability and continuous mathematics, perception, reasoning, learning, and action, and everything from microelectronic devices to robotic planetary explorers. The main unifying theme is the idea of an intelligent agent, aiming to convey the ideas that have emerged over the past fifty years of AI research and the past two millennia of related work. Pseudocode algorithms are included to make the key ideas concrete.
It includes numerous examples, applications, full color images, and human interest boxes to enhance student interest. New chapters on robotics and machine learning are now included. Advanced topics cover neural nets, genetic algorithms, natural language processing, planning, and complex board games.
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