How does Drools Java expert system language and libraries work in Java artificial Intelligence Programming

The primary web site for Drools is www.jboss.org/drools where you can download the source code and documentation.

While there is some interest in using packages like Drools for “business rules” to capture business process knowledge, often as embedded components in large systems, expert systems have historically been built to approach human level expertise for very specific tasks like configuring computer systems and medical diagnosis. The examples in this chapter are very simple and are intended to show you how to embed Drools in your Java applications and to show you a few tricks for using forward chaining rule-based systems. Drools is a Domain Specific Language (DSL) that attempts to provide a syntax that is easier to use than a general-purpose programming language.

It not usually recommend Java IDEs (a personal choice!) but if you already use Eclipse then I suggest that you use the Drools plugins for Eclipse (the “Eclipse Drools Workbench”) which help setting up projects and understand the Drools rule language syntax.

The Eclipse Drools Workbench can automatically generate a small demo which I will go over in some detail in the next two sections. I then design and implement two simple example rule systems for solving block world type problems and for answering help desk questions.

The material in this chapter exclusively covers forward chaining production systems

Figure: Using Drools for developing rule-based systems and then deploying

them.

(also called “expert systems”). Forward chaining systems start with a set of known facts, and apply rules to work towards solving one or more goals. An alternative approach, often used in Prolog programs, is to use backward chaining. Backward chaining systems start with a final goal and attempt to work backwards towards currently known facts.

The phrase, expert systems, was almost synonymous with artificial intelligence in the early and mid 1980s. The application of expert system techniques to real problems, like configuring DEC VAX minicomputers, medical diagnosis, and evaluating seismic data for planning oil exploration had everyone very excited. Unfortunately, expert systems were over hyped and there was an eventual backlash that affected the entire field of AI. Still, the knowledge of how to write expert systems is a useful skill. This chapter contains a tutorial for using the Drools system and also shows how to use machine learning to help generate rules when training data is available.

As seen in Figure, Drools development is interactive: you will work in an environment where you can quickly add and change rules and re-run test cases. This interactive style of development is similar to using PowerLoom as we seen on the blog.