contents

 

       preface

acknowledgments

about this book

about the author

What is artificial intelligence?

A brief history of artificial intelligence

Problem types and problem-solving paradigms

Intuition of artificial intelligence concepts

Uses for artificial intelligence algorithms

What are planning and searching?

Cost of computation: The reason for smart algorithms

Problems applicable to searching algorithms

Representing state: Creating a framework to represent problem spaces and solutions

Uninformed search: Looking blindly for solutions

Breadth-first search: Looking wide before looking deep

Depth-first search: Looking deep before looking wide

Use cases for uninformed search algorithms

Optional: More about graph categories

Optional: More ways to represent graphs

Defining heuristics: Designing educated guesses

Informed search: Looking for solutions with guidance

Adversarial search: Looking for solutions in a changing environment

What is evolution?

Problems applicable to evolutionary algorithms

Genetic algorithm: Life cycle

Encoding the solution spaces

Creating a population of solutions

Measuring fitness of individuals in a population

Selecting parents based on their fitness

Reproducing individuals from parents

Populating the next generation

Configuring the parameters of a genetic algorithm

Use cases for evolutionary algorithms