The final AI application that we will examine is expert systems. These systems are a combination of at least three entities: a database, rules for interpreting the data, and a sophisticated algorithm for searching the database by applying the rules. Usually these systems are very domain specific because of the large amount of rules necessary to represent expert knowledge in a given domain. According to Ashwin Ram  of Georgia Institute of Technology, expert systems exhibit the following characteristics:
- Solve expert problems by expert knowledge, or handle tasks that require detailed knowledge in a particular area.
- Operate in a micro-world where a particular kind of problem solving is required.
- Encapsulates a significant portion of the specialized knowledge that an expert human problem solver would bring to bear.
- Exhibit performance approaching that of an expert.
- Usually built on a production system with certainty values attached to hypotheses.
Now consider an example expert system like MYCIN, one of the first expert systems developed for diagnosing medical problems. According to Alison Cawsey  of Heriot-Watt University, "MYCIN was an expert system developed at Stanford in the 1970s. Its job was to diagnose and recommend treatment for certain blood infections. To do the diagnosis 'properly' involves growing cultures of the infecting organism. Unfortunately this takes around 48 hours, and if doctors waited until this was complete their patient might be dead! So, doctors have to come up with quick guesses about likely problems from the available data, and use these guesses to provide a 'covering' treatment where drugs are given which should deal with any possible problem. MYCIN was developed partly in order to explore how human experts make these rough (but important) guesses based on partial information."
Notice that MYCIN matches the characteristics listed earlier. First, it was designed to solve expert problems in a particular area, namely medicine. Second, it solves a particular kind of problem, namely blood infections. Third, it encapsulates the knowledge that is usually held by an experienced doctor. Fourth, it exhibited performance on the level with human experts. In fact, in some tests, MYCIN even outperformed members of the Stanford medical school! Fifth, MYCIN was built using a system of rules that allowed it to logically compute a diagnosis. These rules represented the "expert knowledge" in a sense. The table below shows one of the many rules in the MYCIN system. Notice that the conclusion (the THEN clause) includes a probability of certainty. Given the preceding facts, the system would make the diagnosis of staphyloccus with 70% confidence.
stain of the organism is gram-positive
AND the morphology of the organism is coccus
AND the growth conformation of the organism is clumps
THEN (0.7) the identity of the organism is staphyloccus
An example rule from the MYCIN system
We can represent the general architecture of expert systems by the diagram below [Ram 1993]. Notice this diagram includes all three parts of our definition: a database, rules for interpreting the data (knowledge base), and a sophisticated algorithm for searching the database by applying the rules (inference engine). For the MYCIN system, the knowledge base consisted of rules similar to the one above. The database of facts would include observations about the blood infection that can be applied to the knowledge base. The inference engine uses the relevant facts and rules to reach an expert diagnosis of the situation.
Some simple expert systems for various topics of interest are available on the web. While these systems do not approach the complexity of commercial expert systems, they still follow the same basic model. The table below lists each of the systems and their purpose. Click on the links below to open the system in a new window, and see if you can identify the parts that correspond to the definition of an expert system.
|Exsel||Expert system for brush and weed control technology selection in Texas.|
|Whale Watcher||Aids in the identification of whales.|
|Pest Identification||Identifies insects which attack Douglas Fir Cones.|
|Grad Admissions Advisor||Expert system application for screening applications for admission to graduate school.|
|Assorted Examples||A collection of expert systems ranging from animal identification to computer trouble shooting.|
- Cawsey, A. (1994), "Databases and Artificial Intelligence Lecture Notes," Department of Computing and Electrical Engineering, Heriot-Watt University, Edinburgh, UK, http://www.cee.hw.ac.uk/~alison/ai3notes/section2_5_5.html.
- Ram, A. (1993), "Artificial Intelligence Lecture Notes," College of Computing, Georgia Institute of Technology, Atlanta, Georgia, http://www.cc.gatech.edu/classes/cs3361_98_winter/expert.txt.