• Author: J. R. Fisher
• Format: HTML (a zipped version is available if you email the author and ask)
• Price: free

This book is a beginners introductory course in Prolog, which is one of the most frequently used programming languages for artificial intelligence. It includes a number of sample programs that are constructed in a top-down, declarative fashion that are meant to help the student to learn the essential basic concepts of Prolog.

Portions of this book date back to 1988, and was originally used to help explain a Prolog interpreter developed by the author. Since then, the author has made a number of revisions and updates to the tutorial, and he continues to edit, expand, and improve it.

Chapters include:

• How to Run Prolog
• Sample Programs
• How Prolog Works
• Built-in Goals
• Search in Prolog
• Logic Topics
• Introduction to Natural Language Processing
• Prototyping with Prolog

http://www.csupomona.edu/~jrfisher/www/prolog_tutorial/

• Author: Thomas Weise
• Format: PDF
• Price: free

This book is about global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on evolutionary computation by discussing evolutionary algorithms, genetic algorithms, genetic programming, learning classifier systems, evolution strategy, differential evolution, particle swarm optimization, and ant colony optimization.

The book also elaborates on other meta-heuristics, such as simulated annealing, hill climbing, tabu search, and random optimization.

According to the author, the book is an ongoing work in progress that has existed for over two years and has been updated and extended throughout that time. However, the book will never be finished since there is always something new to add.

Chapters include:

• Evolutionary Algorithms
• Genetic Algorithms
• Genetic Programming
• Evolution Strategy
• Evolutionary Programming
• Learning Classifier Systems
• Hill Climbing
• Random Optimization
• Simulated Annealing
• Tabu Search
• Ant Colony Optimization
• Particle Swarm Optimization
• Memetic Algorithms
• State Space Search
• Parallelization and Distribution
• Maintaining the Optimal Set
• Benchmarks and Toy Problems
• Contests
• Real-World Applications
• Research Applications
• Sigoa – Implementation in Java
• Set Theory
• Stochastic Theory
• Clustering
• Theoretical Computer Science

http://www.it-weise.de/projects/book.pdf

• Author: Mark Watson
• Format: archived PDF and example code
• Price: free

This Open Content book covers AI programming techniques using Java.

The latest version has a completed new chapter on statistical natural language processing and a new section on embedded expert systems, and a new chapter on spam detection.

This is not the original book written for Morgan Kaufman Publishers. This book contains all new material.

Chapters include:

• Search – graph search, and alpha-beta search in tic-tac-toe and chess
• Natural Language Processing – a simple ATN parser that uses a huge lexicon derived from Wordnet data, material NLBean project, and an embedded Prolog parser (includes Sieuwert van Otterloo’s fine Prolog implementation in Java).
• Expert systems – two simple examples using the Jess system
• Genetic algorithms – Java utility classes and two examples
• Neural Networks – utility classes for Hopfield and back propagation. Only includes simple examples to show how to use the utility classes.
• Statistical Natural Language Processing (Markov Models)
• SPAM Email detection

http://www.markwatson.com/opencontent/javaai_lic.htm