Preface

The latest edition of this book is always available at https://leanpub.com/javaai. You can also download a free copy from my website. Currently the latest edition was released in the spring of 2024.

This edition supports Java version 21 but the example code may have been written in older Java versions and not updated.

The older versions of this book contained examples that have been deprecated and removed. If you want any of those old examples (e.g., natural language interface to relational databses) then the code and PDF for the 4th edition from 2013 can be found here.

I decided which material to keep from old editions and which new material to add based on what my estimation is of which AI technologies are most useful and interesting to Java developers.

I have been developing commercial Artificial Intelligence (AI) tools and applications since the 1980s.

I wrote this book for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical AI programming and information processing techniques. I have tried to make this an enjoyable book to work through. In the style of a “cook book,” the chapters can be studied in any order. When an example depends on a library developed in a previous chapter this is stated clearly. Most chapters follow the same pattern: a motivation for learning a technique, some theory for the technique, and a Java example program that you can experiment with.

The code for the example programs is available on github:

https://github.com/mark-watson/Java-AI-Book

NOTE: this repository contains both example code and the manuscript files for this book.

My Java code in this book can be used under either or both the LGPL3 and Apache 2 licenses - choose whichever of these two licenses that works best for you. Git pull requests with code improvements will be appreciated by me and the readers of this book.

My goal is to introduce you to common AI techniques and to provide you with Java source code to save you some time and effort. Even though I have worked almost exclusively in the field of deep learning in the last six years, I urge you, dear reader, to look at the field of AI as being far broader than machine learning and deep learning in particular. Just as it is wrong to consider the higher level fields of Category Theory or Group Theory to “be” mathematics, there is far more to AI than machine learning. Here we will take a more balanced view of AI, and indeed, my own current research is in hybrid AI, that is, the fusion of deep learning with good old fashioned symbolic AI, probabilistic reasoning, and explainability.

This book is released with a Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. Feel free to share copies of this book with friends and colleagues at work. This book is also available to read free online or to purchase if you want to support my writing activities.

This book will always be available to read free online at https://leanpub.com/javaai/read.

That said, I appreciate it when readers purchase my books because the income enables me to spend more time writing.

I am available for short consulting projects. Please see https://markwatson.com.

I have been interested in AI since reading Bertram Raphael’s excellent book Thinking Computer: Mind Inside Matter in the early 1980s. I have also had the good fortune to work on many interesting AI projects including the development of commercial expert system tools for the Xerox LISP machines and the Apple Macintosh, development of commercial neural network tools, application of natural language and expert systems technology, medical information systems, application of AI technologies to Nintendo and PC video games, and the application of AI technologies to the financial markets. I have also applied statistical natural language processing techniques to analyzing social media data from Twitter and Facebook. I worked at Google on their Knowledge Graph and I managed a deep learning team at Capital One.

I enjoy AI programming, and hopefully this enthusiasm will also infect you, the reader.

The chapter on WordNet uses the examples from the previous chapter on OpenNLP. Both chapters discuss the use of maven to support this code and data sharing.

Additionally, the chapter Statistical Natural Language Processing is configured so the code and linguistic data can be combined with other examples.

Code sharing is achieved by installing the code in your local maven repository, for example:

Now, the code in the OpenNLP example is installed on your system.

My example programs (i.e., the code I wrote) are licensed under the LGPL version 3 and the Apache 2. Use whichever of these two licenses that works better for you. I also use several open source libraries in the book examples and their licenses are:

• Jena Semantic Web: Apache 2
• OpenNlp: Apache 2
• WordNet: MIT style license (link to license)
• Deep Learning for Java (DL4J): Apache 2

My desire is for you to be able to use my code examples and data in your projects with no hassles.

I process the manuscript for this book using the leanpub.com publishing system and I recommend leanpub.com to other authors. Write one manuscript and use leanpub.com to generate assets for PDF, iPad/iPhone, and Kindle versions. It is also simple to push new book updates to readers.

I would like to thank Kevin Knight for writing a flexible framework for game search algorithms in Common LISP (Rich, Knight 1991) and for giving me permission to reuse his framework, rewritten in Java for some of the examples in the Chapter on Search. I would like to thank my friend Tom Munnecke for my photo in this Preface. I have a library full of books on AI and I would like to thank the authors of all of these books for their influence on my professional life. I frequently reference books in the text that have been especially useful to me and that I recommend to my readers.

In particular, I would like to thank the authors of the following two books that have probably had the most influence on me:

• Stuart Russell and Peter Norvig’s Artificial Intelligence: A Modern Approach which I consider to be the best single reference book for AI theory
• John Sowa’s book Knowledge Representation is a resource that I turn to for a holistic treatment of logic, philosophy, and knowledge representation in general

Book Editor: Carol Watson

Thanks to the following people who found typos in this and earlier book editions: Carol Watson, James Fysh, Joshua Cranmer, Jack Marsh, Jeremy Burt, Jean-Marc Vanel