, preface
acknowledgments
about this book
about the author
1. Intuition of artificial intelligence
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
2. Search fundamentals
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
3. Intelligent search
,Defining heuristics: Designing educated guesses
Informed search: Looking for solutions with guidance
Adversarial search: Looking for solutions in a changing environment
4. Evolutionary algorithms
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
5. Advanced evolutionary approaches
Evolutionary algorithm life cycle
Alternative selection strategies
Real-value encoding: Working with real numbers
Order encoding: Working with sequences
Tree encoding: Working with hierarchies
Common types of evolutionary algorithms
Glossary of evolutionary algorithm terms
, More use cases for evolutionary algorithms
6. Swarm intelligence: Ants
What is swarm intelligence?
Problems applicable to ant colony optimization
Representing state: What do paths and ants look like?
The ant colony optimization algorithm life cycle
Use cases for ant colony optimization algorithms
7. Swarm intelligence: Particles
What is particle swarm optimization?
Optimization problems: A slightly more technical perspective
Problems applicable to particle swarm optimization
Representing state: What do particles look like?
Particle swarm optimization life cycle
Use cases for particle swarm optimization algorithms
8. Machine learning
What is machine learning?
Problems applicable to machine learning
A machine learning workflow
Classification with decision trees
Other popular machine learning algorithms
Use cases for machine learning algorithms
9. Artificial neural networks
What are artificial neural networks?
acknowledgments
about this book
about the author
1. Intuition of artificial intelligence
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
2. Search fundamentals
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
3. Intelligent search
,Defining heuristics: Designing educated guesses
Informed search: Looking for solutions with guidance
Adversarial search: Looking for solutions in a changing environment
4. Evolutionary algorithms
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
5. Advanced evolutionary approaches
Evolutionary algorithm life cycle
Alternative selection strategies
Real-value encoding: Working with real numbers
Order encoding: Working with sequences
Tree encoding: Working with hierarchies
Common types of evolutionary algorithms
Glossary of evolutionary algorithm terms
, More use cases for evolutionary algorithms
6. Swarm intelligence: Ants
What is swarm intelligence?
Problems applicable to ant colony optimization
Representing state: What do paths and ants look like?
The ant colony optimization algorithm life cycle
Use cases for ant colony optimization algorithms
7. Swarm intelligence: Particles
What is particle swarm optimization?
Optimization problems: A slightly more technical perspective
Problems applicable to particle swarm optimization
Representing state: What do particles look like?
Particle swarm optimization life cycle
Use cases for particle swarm optimization algorithms
8. Machine learning
What is machine learning?
Problems applicable to machine learning
A machine learning workflow
Classification with decision trees
Other popular machine learning algorithms
Use cases for machine learning algorithms
9. Artificial neural networks
What are artificial neural networks?
