Foundations of Artificial Intelligence

Aleksandr Shitik
Aleksandr Shitik

I write my own posts and books, and review movies and books. Expert in cosmology and astronomy, IT, productivity, and planning.

Foundations of Artificial Intelligence
E. Borovskaya, N. Davydova
Genres: Information Technology (IT)
Year of publication: 2020
Year of reading: 2022
My rating: Bad
Number of reads: 1
Total pages: 130
Summary (pages): 0
Original language of publication: Russian
Translations to other languages: No translations to other languages found

General Description

A concise book of three chapters. The material is presented mainly as strictly textual information, though occasional diagrams or code snippets occasionally appear. The book is readable – easy but dull.

Brief Summary

Here are the main topics of this book, taken from the table of contents:

Chapter 1. Artificial Intelligence

  • 1.1. Introduction to AI Systems
  • 1.1.1. The Concept of Artificial Intelligence
  • 1.1.2. AI in Russia
  • 1.1.3. Functional Structure of an AI System
  • 1.2. Directions of AI Development
  • 1.3. Data and Knowledge. Knowledge Representation in Intelligent Systems
  • 1.3.1. Data and Knowledge: Key Definitions
  • 1.3.2. Knowledge Representation Models
  • 1.4. Expert Systems
  • 1.4.1. Structure of an Expert System
  • 1.4.2. Development and Use of Expert Systems
  • 1.4.3. Classification of Expert Systems
  • 1.4.4. Knowledge Representation in Expert Systems
  • 1.4.5. Toolsets for Building Expert Systems
  • 1.4.6. Expert‑System Development Technology
  • Review Questions and Exercises for Chapter 1
  • References for Chapter 1

Chapter 2. Logic Programming

  • 2.1. Programming Methodologies
  • 2.1.1. Imperative Programming Methodology
  • 2.1.2. Object‑Oriented Programming Methodology
  • 2.1.3. Functional Programming Methodology
  • 2.1.4. Logic Programming Methodology
  • 2.1.5. Constraint Programming Methodology
  • 2.1.6. Neural‑Network Programming Methodology
  • 2.2. Short Intro to Predicate Calculus and Theorem Proving
  • 2.3. Logical Inference Process in Prolog
  • 2.4. Structure of a Prolog Program
  • 2.4.1. Using Composite Objects
  • 2.4.2. Using Alternate Domains
  • 2.5. Organizing Recursion in Prolog
  • 2.5.1. Back‑tracking After Failure
  • 2.5.2. Cut & Back‑tracking
  • 2.5.3. Simple Recursion
  • 2.5.4. Generalized Recursion Rule (GRR)
  • 2.6. Lists in Prolog
  • 2.6.1. List Operations
  • 2.7. Strings in Prolog
  • 2.7.1. String Operations
  • 2.8. Files in Prolog
  • 2.8.1. Prolog File Predicates
  • 2.8.2. File Domain Description
  • 2.8.3. Writing to a File
  • 2.8.4. Reading from a File
  • 2.8.5. Modifying an Existing File
  • 2.8.6. Appending to an Existing File
  • 2.9. Introducing Dynamic Databases in Prolog
  • 2.9.1. Prolog Databases
  • 2.9.2. Dynamic‑Database Predicates in Prolog
  • 2.10. Building Expert Systems
  • 2.10.1. Expert‑System Structure
  • 2.10.2. Knowledge Representation
  • 2.10.3. Inference Methods
  • 2.10.4. User‑Interface System
  • 2.10.5. Rule‑Based Expert System
  • Review Questions and Exercises for Chapter 2
  • References for Chapter 2

Chapter 3. Neural Networks

  • 3.1. Introduction to Neural Networks
  • 3.2. Artificial Neuron Model
  • 3.3. Neural‑Network Applications
  • 3.4. Training a Neural Network
  • Review Questions and Exercises for Chapter 3
  • References for Chapter 3

Opinion

If someone asked me for the most useless book I’ve read in the last few years, I’d definitely pick this one. In fact, it isn’t even a book – it’s a dry university hand‑book, as useless as they come, all theory and no practice.

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