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What is Artificial Intelligence?

Artificial Intelligence (AI) is the study of computer systems and devices that simulate or
operate in a way that is usually associates with human intelligence, such as learning, reasoning, intelligent processing, understanding symbolic information and pattern recognition, and producing knowledge. Although AI is commonly viewed as a branch of computer science, AI is multi-disciplinary because it combines several branches of learning including algorithms, heuristics, databases, artificial languages, natural language processing, and theoretical computer science. It also has close ties with psychology, cognitive psychology, neuroscience, biology, mathematical logic, system studies, business intelligence, and knowledge management.

AI also is concerned with problem solving and developing programs that achieve performance comparable to a human expert in solving problems in a particular domain or area of application, or about objects, events, situations, or a unit, by drawing inferences from information and facts from a knowledge base by human experts.

There are many organizations, associations, societies, Websites, and enthusiasts from around the world that devote themselves to the advancement of AI. Also, there are some professionals in business and industry, and professors and students who are thinking about starting an AI group, an AI-project or course. So, to get them started , I am presenting below a list of recommended books for reading and exploring some concepts and techniques of artificial intelligence. They also can request a copy of my article, New Prospects for Artificial Intelligence from klorimer @linkframe.com

Artifical Intelligence: A Modern Approach--Sturart J. Russell, et al Artificial Intelligence: A New Synthesis--Nils J. Nilsson
Artificial Intelligence-- Patrick Henry Winston Artificial Intelligence: Theory and Practice-- Thomas Dean, et al
Artificial Intelligence & Mobile Robots-- David Kortenkamp (Editor), et al Artificial Intelligence: Structures and Strategies for Complex Problem Solving-- George F. Luger, et al
Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodologh, Tool and Case Studies--Gheorghe Tecuci Common Lisp: The Language--Guy L., Jr. Steele
The Computational Brain (Computational Neuroscience)--Patricia S. Churchland, et al Darwin Among the Machines: The Evolution of Global Intelligence--George B. Dyson
Developing Intelligent Agents for Distributed Systems: Exploring Architecture, Technologies, and Applications--Michael Knapik, et al The Emperor's New Minds, and the Laws of Physics-- Roger Penrose
Essentialls of Artificial Intelligence-- Matt Ginsberg
Case-Based Reasoning-- Janet L. Kolodner Expert Systems: Design and Development-- Jack Durkin
Developing Your First Expert System --J. Liebowitz and C. Letsky Expert Systems: Real World Applications--Annabel Beerel
Fundamentals of neural Networks--Laurence V. Fausett Handbook on Expert Systems--J. Liebowitz
Genetic Algorithms in Search, Optimization and Machine Learning-- David E. Goldberg Genetic Programming: An Introduction on the Automatic Evolution of Computer Programs and its Applications--Wolfgang Banzhaf, et al
Godel, Escher, Bach: An Eternal Golden Braid-- Douglas R. Hofstadter How to  Build a Speech Recognition Application-- Bruce Balentine, et al
Intelligent Software Agents-- Richard Murch, et al Introduction to Artificial Neural Systems-- Jack M. Zurada
Introduction to The Theory of Neural Computation (Santa Fe Institute Studies in the Sciences of Complexity, Lecture Notes, Vol.1 )-- Andrew Krogh, et al The Handbook of Brain Theory and Neural Networks-- Michael Arbib, et al
Knowledge Engineering and Management: The CommonKADS Methodology-- Guus Schreiber, et al Knowledge Management Toolkit: The Practical Techniques for a Knowledge Management System-- Amrit Tiwana
The Fifth Generation--Feigenbaum and McCorduck Rule-Based Expert Systems--Buchanan .B and Shortliffe. E
Artificial Intelligence and Instruction-- Kearsley, G. et al Logical Foundations of Artificial Intelligence-- Michael R. Genesereth, et al
Machine Learning (McGraw-Hill Series in Computer Science--Tom Mitchell Education knowledge and the Computer-- Kenneth V. Lorimer Lisp-- Patrick Henry Winston
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence-- JacQues Ferber Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp-- Peter Norvig
Object Oriented Programming in Copmmon Lisp: A Programmers Guide to the Common Lisp Object System-- Sonya Keene Readings in Agents-- Michael N Huhns, et al
Reasoning About Rational Agents (Intelligent Robotics and Autonomous Agents-- Michael J. Woolridge Rethinking Innateness: A Connectionist Perspective on Development (Neural Networks and Connectionist Modeling Series)-- Jeffrey. Elman et al
Object- Oriented Neural Network in C++ --Joey Rogers Prolog Programming for Artificial Intelligence-- Ivan Bratko
Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)--Richard Sutton, et al Self-Organizing Maps (Springer Series in Information Sciences, 30)-- Teuvo Kohonen, et al
Speech Recognition: Theory and C++ Implementation-- Claudio Becchetti, et al Software Agents-- Jeffrey M Bradshaw, et al
The Mind Within the Net: Models of Learning, Thinking, and Acting-- Manfred Spitzer Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations-- Ian Witten
Understanding Computers and Cognition: A New Foundation for Design-- Terry Winograd Understanding Intelligence-- Rolf Pfeifer and Christian Scheier

 

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