Proudly Serving Utah

---

Utah Expert Systems Since 1996

Make critical decisions through intelligent automated learning.

In my opinion after being in this field since 1980 and seeing technologies build on one another Artificial Intelligence (AI) was not possible without the stepping stones of Knowledge Based System to expert systems, machine learning and faster technologies with more disk space.  I utilized these same approaches in building self healing systems which was specifically good in disaster recover and notifications around automated detection and resolution where possible.

Utah Customers

Some information I know about Utah is I believe the state was admitted or ratified to the United States around or about 'January 4, 1896'.  Utah is located around latitude '39.41922' and longitude of '-111.950684' and has a population of roughly '3,271,616 million'.  If I remember correctly the capital is 'Salt Lake City' and the largest city is 'Salt Lake City'. 

What is an Expert System?

An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.  AI is nothing without knowledge bases, inference engines, explanation facilities, knowledge acquisition facilities, and user interfaces.

Expert System Usage

The goal of knowledge-based systems is to make the critical information required for the system to work explicit rather than implicit.  In a traditional computer program the logic is embedded in code that can typically only be reviewed by an IT specialist.  With an expert system the goal was to specify the rules in a format that was intuitive and easily understood, reviewed, and even edited by domain experts rather than IT experts.  The benefits of this explicit knowledge representation were rapid development and ease of maintenance.

Expert System Components

Expert systems were the first commercial systems to use a knowledge-based architecture.  In general view, an expert system includes the following components: a knowledge base, an inference engine, an explanation facility, a knowledge acquisition facility, and a user interface.

There is an inference engine which is an automated reasoning system that evaluates the current state of the knowledge-base, applies relevant rules, and then asserts new knowledge into the knowledge base.  The inference engine may also include abilities for explanation, so that it can explain to a user the chain of reasoning used to arrive at a particular conclusion by tracing back over the firing of rules that resulted in the assertion.

If at any point you decide to reach to me just know the area codes I am familiar with for Utah are '385, 435, 801'.  For Expert Systems assistance you will find my rates very reasonable for Utah.  Now just keep in mind my time zone is 'Eastern Standard Time (EST)' and I know the time zones in Utah are 'Mountain Standard Time (MST)' in case you wish to call me.  Anyway let me continue.

Expert System Techniques

Expert systems involve various techniques for inferencing engines.  They are as follows:

1. Truth maintenance - These systems record the dependencies in a knowledge-base so that when facts are altered, dependent knowledge can be altered accordingly.  For example, if the system learns that Socrates is no longer known to be a man it will revoke the assertion that Socrates is mortal.
2. Hypothetical reasoning - In this, the knowledge base can be divided up into many possible views, a.k.a.  worlds.  This allows the inference engine to explore multiple possibilities in parallel.  For example, the system may want to explore the consequences of both assertions, what will be true if Socrates is a Man and what will be true if he is not?
3. Uncertainty systems - One of the first extensions of simply using rules to represent knowledge was also to associate a probability with each rule.  So, not to assert that Socrates is mortal, but to assert Socrates may be mortal with some probability value.  Simple probabilities were extended in some systems with sophisticated mechanisms for uncertain reasoning, such as Fuzzy logic, and combination of probabilities.
4. Ontology classification - With the addition of object classes to the knowledge base, a new type of reasoning was possible.  Along with reasoning simply about object values, the system could also reason about object structures.  In this simple example, Man can represent an object class and R1 can be redefined as a rule that defines the class of all men.  These types of special purpose inference engines are termed classifiers.  Although they were not highly used in expert systems, classifiers are very powerful for unstructured volatile domains, and are a key technology for the Internet and the emerging Semantic Web

You know, I don't make it out to Utah much but I would like to see the 'American Seagull' state bird.  I am a little familiar with the Utah 'Sego lily' state flower as well.  However, I do not know much about Utah's state tree the 'Blue Spruce'.  Fishing is fun to me perhaps I would like reeling in the Utah 'Bonneville Cutthroat Trout' state fish.  Anyway, sorry I went off topic.  Let me continue.

Disadvantages of Expert Systems

Essentially the worst disadvantage was that of critical knowledge acquisition then came the disadvantages of integration, access to large databases, and performance.  Complexity also be came an issue where say 100 million rules sounds good but became too complicated for computational processing.  Overfitting and overgeneralization effects when using known facts and trying to generalize to other cases not described explicity in the knowledgebase created problems too.  This is where machine learning was employed to assist.  It was then realized that new approaches to Artificial Intelligence (AI) were required instead of rule-based technologies.