The LORDEX Advantage

The Lorins Diagnostic & Tutorial Expert (LORDEX) Systems approach was developed by Mr. Pete Lorins, an engineer, educator, entrepreneur, spiritual leader, and lawyer who was convinced that there is a better way to diagnose problems, and certainly a better way to learn and essentially a better way to train others.

Indeed, whether you are involved in:

• The Law;

• Education;

• Business Development or Management;

• Technology;

• Science;

• Politics;

• etc.

There will come a time where you'll have to train new and existing clients on known knowledge that either you or one of your affiliates as Subject Matter Experts (SMEs) have developed expertise in. And at that point, it would often not be practical to, recurrently, train new employees yourself or pay someone else to do the training when you can use one of our LORDEX expert systems to save you time and money. At LORDEX Systems, we are prepared to develop expert systems that will be customized and tailored to meet either your needs or that of your company or its affiliates.

Advantages Reaped Using an Expert System*

• Increased Availability: the expert system will act as your "always available expert" as it will be constantly available on any suitable computer.

• Reduced Cost: The cost of providing expertise per user is greatly lowered.

• Reduced Danger: Expert systems can be used in environments that might be hazardous to humans.

• Permanence: Unlike human experts who may retire, quit, or die, the expert system's knowledge will last indefinitely.

• Multiple Expertise: The knowledge of multiple experts can be made available to work simultaneously and continuously on a problem at any time of day or night. The level of expertise combined from several experts may exceed that of a single human expert (Harmon 85).

• Increased Reliability: Expert systems increase confidence that the correct decision was made by providing a second opinion to a human expert or break a tie in case of disagreements by multiple human experts.

• Explanation: The expert system can explicitly explain in detail the reasoning that led to a conclusion. A human may be too tired, unwilling. or unable to do this all the time. This increases the confidence that the correct decision is made.

• Fast Response: Fast or real-time response may be necessary for some applications. Depending on the software and hardware used, an expert system is likely to respond faster and be more available than an human expert. Some emergency situations may require responses faster than a human and so a real-time expert system is a good choice (Hug 88; Ennis 86).

• Steady, Unemotional, and Complete Responses at All Times: This may be very important in real-time and emergency situations, when a human expert may not operate at peak efficiency because of stress or fatigue.

• Intelligent Tutor: The expert system may act as an intelligent tutor by letting the student run sample programs and by explaining the system's reasoning.

• Intelligent Database: Expert systems can be used to access a database in an intelligent manner (Kerschberg 86; Schur 88)

* as accentuated by Giarratano and Riley

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Familiarity With Important LORDEX Systems Terms

As bottom-lined by Giarratano and Riley, each expert system is comprised of a combination of the following components:

1) User Interface: the mechanism by which the user and the expert system communicate;

2) Explanation Facility/Subsystem: explains the reasoning of the system to the user;

3) Knowledge Base or Working Memory: a database of "facts" or Heuristics used by the rules;

4) Inference Engine/Mechanism: makes inferences by deciding which rules are satisfied by facts or objects, prioritizes the satisfied rules, and executes (fires) the rule with the highest priority;

5) Agenda: a prioritized list of rules created by the inference engine, whose patterns are satisfied by facts or objects in "working memory" (i.e., the Knowledge Base);

6) Knowledge Acquisition Facility/Subsystem: an automatic way for the user to enter knowledge in the system instead of having the knowledge engineer explicitly code the knowledge.

Most of our tools, whether command-line or object-oriented/graphics-driven use a variant of the well known and effective "rete algorithm" to fire/process the underlying rules of your respective expert system.

The Explanation Facility/Subsystem may be simple or elaborate. A Simple Explanation Facility in a rule-based system may list all the facts that made the latest rule execute. However, a more Elaborate Explanation Facility may do the following:

• Give a prognosis or prediction of what will occur if a hypothesis is true.

• Explain all the consequences of a hypothesis.

• List all the hypotheses that may explain the observed evidence.

• List all the reasons for and against a particular hypothesis.

• Justify the questions that the program asks of the user for further information. These questions may be used to direct the line of reasoning to likely diagnostic paths

• Justify the knowledge of the program.

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Basic Components of an Expert System Shell or Tool (source - wtec.org)

Expert System Shell or Tool

A software tool used to create expert systems. There is a plethora of expert system tools out there, and each expert system shell/tool is suitable to a particular range of projects.

Expert Systems

A branch of Artificial Intelligence (AI) that makes extensive use of specialized knowledge to solve problems at the level of a human expert. Expert systems are generally designed different from conventional programs because the problems usually have no algorithmic solution, thus rely on inferences to achieve a reasonable solution.

Knowledge-based Systems

It is a a term that is better used for the application of knowledge-based technology. But today, it is used to refer the creation of either expert systems or knowledge based systems.

Rule Induction

The process of allowing the system to learn rules by example, in which the system creates rules from tables of data.

Knowledge Engineer

The professional who explicitly codes the knowledge into the expert system after retrieving it from an expert or another source. The knowledge engineer acts as an intermediary between the specialist and the expert system.

Knowledge Engineering

The process of building an expert system.

Causal Knowledge

Understanding of the underlying causes and effects in a system - today, this is a practical limitation of many expert systems.

Shallow Knowledge

Empirical and heuristic knowledge. Heuristics are rules of thumb  or empirical knowledge gained from experience that may aid in the solution, but are not always guaranteed to work.

Deep Knowledge

Knowledge that's based on the basic structures, functions and behaviors of objects.

Metarule

Knowledge about the rules.

Incremental Growth of Knowledge

In a rule-based system, knowledge can easily grow incrementally (i.e., little by little as rules are added) so that the correctness of the system can be continually checked.  This facilitates rapid prototyping so that the knowledge engineer can quickly show the expert a working prototype of the expert system.

Some other Areas of Artificial Intelligence (AI):

• Robotics;

• Vision;

• Natural Language;

• Speech;

• Artificial Neural Systems;

• Natural Language;

• Understanding;

• Etc.

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Contact Information:

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