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Breathe intelligence into Java

Making AI work in your Java programs is easier than you think

By Lane W. Sharman, JavaWorld.com, 04/06/01

Evolutionary computation is crucial for the next generation of applications. Evolutionary computation is software that adds and removes methods, parameters, and iterators, as well as generally modifies the elements of an algorithm in response to a fitness test: Did algorithm i yield a better fitness result than algorithm j, where fitness is time, distance, or some other value measure? The field of evolutionary computation, while shrouded in academia, provides the gateway for writing smart, adaptive, and self-evolving Java applications. Sadly, Sun Microsystems has invested thousands of man-hours in APIs related to dashboard technology and has yet to show a serious initiative for bringing artificial intelligence (AI) into the Java mainstream. Support for such an effort would make for a tremendous step in the advancement of Java computing.

For now, I will examine the basic concepts, look at the wealth of resources, and show how I am developing a Java component in the area of security and authentication using AI technology. Making AI work in your Java programs is easier than you think.

Terminology and concepts

The framework for developing an intelligent Java component requires the application of key concepts and discerning the branches of AI:

Statement of problem

Most important, an evolved component does something intelligently. The statement of what it does, not how it does it, is the important part. In 1959 Arthur Samuel asked, "How can computers be made to do what needs to be done, without being told exactly how to do it." Establishing the tableau, "what needs to be done" is shared between the domain expert and the engineer; for example, "find all schedules that get the project done in less than n months without exceeding cost X using human resources Y and machines Z." Another example, germane to the subject of this article, "find all users who have stolen the credential of a valid user within an error rate of 10 percent."

Fitness

You will require a fitness measure for your intelligent component. There must be some standard; for example, your component must have authentication errors fewer than 0.1 percent, mean time between failure (MTBF) greater than 90 days, least amount of time, lowest cost, shortest distance, and so forth.

Training set

You should employ a training set with known values to test the fitness of your Java component. Training sets are data sets that measure known fitness against a specific instance of an algorithm undergoing evolution. The program evolves against the fitness tests where each generation of solutions improves over time (milliseconds not eons).

The training set then provides the right answers for a given individual Java instance. If the instance is wrong for many of the training set elements, it is less likely to be combined with other instances into the next generation.

A training set is sometimes provided as part of the fitness test but not always. For example, for an algorithm to evolve, it may need an external training set to check its results. For classification components, the training set tells the fitness evaluator if the classification was right or wrong.

 

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