Principles of Artificial Intelligence

Morgan Kaufmann
Free sample

A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.

Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.

Read more
Collapse

About the author

Nils J. Nilsson's long and rich research career has contributed much to AI. He has written many books, including the classic Principles of Artificial Intelligence. Dr. Nilsson is Kumagai Professor of Engineering, Emeritus, at Stanford University. He has served on the editorial boards of Artificial Intelligence and Machine Learning and as an Area Editor for the Journal of the Association for Computing Machinery. Former Chairman of the Department of Computer Science at Stanford, and former Director of the SRI Artificial Intelligence Center, he is also a past president and Fellow of the American Association for Artificial Intelligence.

Read more
Collapse
Loading...

Additional Information

Publisher
Morgan Kaufmann
Read more
Collapse
Published on
Jun 28, 2014
Read more
Collapse
Pages
476
Read more
Collapse
ISBN
9781483295862
Read more
Collapse
Read more
Collapse
Best For
Read more
Collapse
Language
English
Read more
Collapse
Genres
Computers / Data Processing
Computers / Expert Systems
Computers / General
Computers / Intelligence (AI) & Semantics
Read more
Collapse
Content Protection
This content is DRM protected.
Read more
Collapse
Eligible for Family Library

Reading information

Smartphones and Tablets

Install the Google Play Books app for Android and iPad/iPhone. It syncs automatically with your account and allows you to read online or offline wherever you are.

Laptops and Computers

You can read books purchased on Google Play using your computer's web browser.

eReaders and other devices

To read on e-ink devices like the Sony eReader or Barnes & Noble Nook, you'll need to download a file and transfer it to your device. Please follow the detailed Help center instructions to transfer the files to supported eReaders.
This new edition provides a comprehensive, colorful, up-to-date, and accessible presentation of AI without sacrificing theoretical foundations. 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. A companion DVD is provided with resources, applications, and figures from the book. Numerous instructors’ resources are available upon adoption.

eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at info@merclearning.com.

FEATURES:

• Includes new chapters on robotics and machine learning and new sections on speech understanding and metaphor in NLP

• Provides a comprehensive, colorful, up to date, and accessible presentation of AI without sacrificing theoretical foundations

• Uses numerous examples, applications, full color images, and human interest boxes to enhance student interest

• Introduces important AI concepts e.g., robotics, use in video games, neural nets, machine learning, and more thorough practical applications

• Features over 300 figures and color images with worked problems detailing AI methods and solutions to selected exercises

• Includes DVD with resources, simulations, and figures from the book

• Provides numerous instructors’ resources, including: solutions to exercises, Microsoft PP slides, etc.
Although many texts exist offering an introduction to artificial intelligence (AI), this book is unique in that it places an emphasis on knowledge representation (KR) concepts. It includes small-scale implementations in PROLOG to illustrate the major KR paradigms and their developments.****back cover copy:**Knowledge representation is at the heart of the artificial intelligence enterprise: anyone writing a program which seeks to work by encoding and manipulating knowledge needs to pay attention to the scheme whereby he will represent the knowledge, and to be aware of the consequences of the choices made.****The book's distinctive approach introduces the topic of AI through a study of knowledge representation issues. It assumes a basic knowledge of computing and a familiarity with the principles of elementary formal logic would be advantageous.****Knowledge Representation: An Approach to Artificial Intelligence develops from an introductory consideration of AI, knowledge representation and logic, through search technique to the three central knowledge paradigms: production rules, structured objects, and predicate calculus. The final section of the book illustrates the application of these knowledge representation paradigms through the Prolog Programming language and with an examination of diverse expert systems applications. The book concludes with a look at some advanced issues in knowledge representation.****This text provides an introduction to AI through a study of knowledge representation and each chapter contains exercises for students. Experienced computer scientists and students alike, seeking an introduction to AI and knowledge representations will find this an invaluable text.
What beliefs are, what they do for us, how we come to hold them, and how to evaluate them.

Our beliefs constitute a large part of our knowledge of the world. We have beliefs about objects, about culture, about the past, and about the future. We have beliefs about other people, and we believe that they have beliefs as well. We use beliefs to predict, to explain, to create, to console, to entertain. Some of our beliefs we call theories, and we are extraordinarily creative at constructing them. Theories of quantum mechanics, evolution, and relativity are examples. But so are theories about astrology, alien abduction, guardian angels, and reincarnation. All are products (with varying degrees of credibility) of fertile minds trying to find explanations for observed phenomena. In this book, Nils Nilsson examines beliefs: what they do for us, how we come to hold them, and how to evaluate them. We should evaluate our beliefs carefully, Nilsson points out, because they influence so many of our actions and decisions.

Some of our beliefs are more strongly held than others, but all should be considered tentative and changeable. Nilsson shows that beliefs can be quantified by probability, and he describes networks of beliefs in which the probabilities of some beliefs affect the probabilities of others. He argues that we can evaluate our beliefs by adapting some of the practices of the scientific method and by consulting expert opinion. And he warns us about “belief traps”—holding onto beliefs that wouldn't survive critical evaluation. The best way to escape belief traps, he writes, is to expose our beliefs to the reasoned criticism of others.

©2019 GoogleSite Terms of ServicePrivacyDevelopersArtistsAbout Google|Location: United StatesLanguage: English (United States)
By purchasing this item, you are transacting with Google Payments and agreeing to the Google Payments Terms of Service and Privacy Notice.