contents
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