Artificial Intelligence by Aggarwal, Charu C.;

A Textbook

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Product details:

Edition number 1st ed. 2021
Publisher Springer
Date of Publication 18 July 2022
Number of Volumes 1 pieces, Book

ISBN 9783030723590
Binding Paperback
No. of pages483 pages
Size 254x178 mm
Weight 957 g
Language English
Illustrations 158 Illustrations, black & white; 15 Illustrations, color

Short description:

This textbook covers the broader field of artificial intelligence. The chapters for this textbook span within three categories:

Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions. The underlying methods include search and logic-based methods. These methods are discussed in Chapters 1through 5.

Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses. Examples include regression modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These methods are discussed in Chapters~6 through 11.

Integrating Reasoning and Learning: Chapters~11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence.

The primary audience for this textbook are professors and advanced-level students in computer science. It is also possible to use this textbook for the mathematics requirements for an undergraduate data science course. Professionals working in this related field many also find this textbook useful as a reference.

Long description:

This textbook covers the broader field of artificial intelligence. The chapters for this textbook span within three categories:

Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions. The underlying methods include search and logic-based methods. These methods are discussed in Chapters 1through 5.

Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses. Examples include regression modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These methods are discussed in Chapters~6 through 11.

Integrating Reasoning and Learning: Chapters~11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence.

?The author has thoroughly researched all areas of AI in order to write this high-quality book. ? This highly valuable book provides a vast overview of AI in a well-structured manner. It could be used as a textbook in graduate-level courses.? (J. Arul, Computing Reviews, December 12, 2022)

?This is very useful book for graduate students and researchers.? (T. C. Mohan, zbMATH 1477.68001, 2022)

1 An Introduction to Artificial Intelligence.- 2 Searching State Spaces.- 3 Multiagent Search.- 4 Propositional Logic.- 5 First-Order Logic.- 6 Machine Learning: The Inductive View.- 7 Neural Networks.- 8 Domain-Specific Neural Architectures.- 9 Unsupervised Learning.- 10 Reinforcement Learning.- 11 Probabilistic Graphical Models.- 12 Knowledge Graphs.- 13 Integrating Reasoning and Learning.