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