Logic Programming Languages for Expert Systems

Abstract

“Expert systems” are a significant subset of what is known as “decision support systems” (DSS). This article suggests a different paradigm for expert systems than what is commonly used. Most often, expert systems are developed with a tool called an “expert system shell.” For the more adventurous, an expert system might be developed with Prolog, a language for artificial intelligence. Both Prolog and expert system shells stem from technology that is approximately 30 years old.1 There have been updates to these platforms, such as GUI interfaces, XML interfaces, and other “bells and whistles.” However, the technology is still fundamentally old. As an analogy, the current technology is akin to updating a 30-year-old car with new paint (a gooey interface), new upholstery, GPS, and so forth. However, the car is fundamentally still a 30-year-old car. It may be in far better shape than another 30-year-old car without the updates, but it cannot compete from an engineering perspective with current models.2 Similarly, the reasoning power of current expert system technology cannot compete with the reasoning power of the state of the art in logic programming. These advances that have taken place in the logic programming community since the advent of Prolog and expert system shells include: a well developed theory of multiple forms of negation, an understanding of open domains, and the closed world assumption, default reasoning with exceptions, reasoning with respect to time (i.e., a solution to the frame problem and introspection with regard to previous beliefs), reasoning about actions, introspection, and maintaining multiple views of the world simultaneously (i.e., reasoning with uncertainty). This article examines a family of logic programming languages. This article in conjunction with a companion article this volume, Knowledge Representation That Can Empower Expert Systems, suggest that logic programs employing recent advances in semantics and in knowledge representation provide a more robust framework in which to develop expert systems. The author has successfully applied this paradigm and these ideas to financial applications, security applications, and enterprise information systems.