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Artificial Intelligence with Python

You're reading from   Artificial Intelligence with Python Your complete guide to building intelligent apps using Python 3.x

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781839219535
Length 618 pages
Edition 2nd Edition
Languages
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Authors (2):
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Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
Alberto Artasanchez Alberto Artasanchez
Author Profile Icon Alberto Artasanchez
Alberto Artasanchez
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Toc

Table of Contents (26) Chapters Close

Preface 1. Introduction to Artificial Intelligence 2. Fundamental Use Cases for Artificial Intelligence FREE CHAPTER 3. Machine Learning Pipelines 4. Feature Selection and Feature Engineering 5. Classification and Regression Using Supervised Learning 6. Predictive Analytics with Ensemble Learning 7. Detecting Patterns with Unsupervised Learning 8. Building Recommender Systems 9. Logic Programming 10. Heuristic Search Techniques 11. Genetic Algorithms and Genetic Programming 12. Artificial Intelligence on the Cloud 13. Building Games with Artificial Intelligence 14. Building a Speech Recognizer 15. Natural Language Processing 16. Chatbots 17. Sequential Data and Time Series Analysis 18. Image Recognition 19. Neural Networks 20. Deep Learning with Convolutional Neural Networks 21. Recurrent Neural Networks and Other Deep Learning Models 22. Creating Intelligent Agents with Reinforcement Learning 23. Artificial Intelligence and Big Data 24. Other Books You May Enjoy
25. Index

Constraint satisfaction problems

There are many problems that must be solved under constraints. These constraints are basically conditions that cannot be violated during the process of solving the problem.

These problems are referred to as Constraint Satisfaction Problems (CSPs).

To gain some intuitive understanding, let's quickly look at an example section of a Sudoku puzzle. Sudoku is a game where we cannot have the same number twice across a horizontal line, vertical line, or in the same square. Here is an example Sudoku board:

A black and silver text on a white surface  Description automatically generated

Figure 1: Example of a Sudoku board

Using constraint satisfaction and the rules of Sudoku we can quickly determine which numbers to try and which numbers not to try to solve the puzzle. For example, in this square:

Figure 2: Considering a problem in Sudoku

If we were not using CSP, one brute force approach would be to try all of the combinations of numbers in the slots and then check if the rules applied. For example, our...

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