Expert systems

An expert system is a class of computer programs developed by researchers in artificial intelligence during the 1970s and applied commercially throughout the 1980s. In essence, they are programs made up of a set of rules that analyze information (usually supplied by the user of the system) about a specific class of problems, as well as provide analysis of the problem(s), and, depending upon their design, recommend a course of user action in order to implement corrections.

A related term is wizard (software). Like an expert system, a wizard is also an interactive computer program that helps a user solve a problem. Usually, the term wizard is used for programs that search a database for criteria entered by the user. Unfortunately, the distinction between these two definitions is not universal, and some rule-based programs are called wizards. Other "Wizards" are simply online forms that display a series of menus for the user to facilitate choices, such as the ones which manage the installation of new software on computers, and these clearly are not Expert Systems.

Types of problems solved by expert systems
Typically, the problems to be solved are of the sort that would normally be tackled by a human "expert"&mdash;a medical or other professional, in most cases. Real experts in the problem domain (which will typically be very narrow, for instance "diagnosing skin diseases in human teenagers") are asked to provide "rules of thumb" on how they evaluate the problems, either explicitly with the aid of experienced system developers, or sometimes implicitly, by getting such experts to evaluate test cases and using computer programs to examine the test data and (in a strictly limited manner) derive rules from that. Generally expert systems are used for problems for which there is no single "correct" solution which can be encoded in a conventional algorithm &mdash; one would not write an expert system to find shortest paths through graphs, or sort data, as there are simply easier ways to do these tasks.

Simple systems use simple true/false logic to evaluate data, but more sophisticated systems are capable of performing at least some evaluation taking into account real-world uncertainties, using such methods as fuzzy logic. Such sophistication is difficult to develop and still highly imperfect.

Application
While expert systems have distinguished themselves in AI research in finding practical application, their application has been limited. Expert systems are notoriously narrow in their domain of knowledge&mdash;as an amusing example, a researcher used the "skin disease" expert system to diagnose his rustbucket car as likely to have developed measles&mdash;and the systems were thus prone to making errors that humans would easily spot. Additionally, once some of the mystique had worn off, most programmers realized that simple expert systems were essentially just slightly more elaborate versions of the decision logic they had already been using. Therefore, some of the techniques of expert systems can now be found in most complex programs without any fuss about them.

Another 1970s and 1980s application of Expert Systems &mdash; which we today would simply call AI &mdash; was in computer games. For example, the computer baseball games Earl Weaver Baseball and Tony La Russa Baseball each had highly detailed simulations of the game strategies of those two baseball managers. When a human played the game against the computer, the computer queried the Earl Weaver or Tony La Russa Expert System for a decision on what strategy to follow. Even those choices where some randomness was part of the natural system (such as when to throw a surprise pitch-out to try to trick a runner trying to steal a base) were decided based on probabilities supplied by Weaver or La Russa. Today we would simply say that "the game's AI provided the opposing manager's strategy."

Expert systems versus problem-solving systems
The principal distinction between expert systems and traditional problem solving programs is the way in which the problem related expertise is coded. In traditional applications, problem expertise is encoded in both program and data structures.

In the expert system approach all of the problem related expertise is encoded in data structures only; none is in programs. Several benefits immediately follow from this organization.

An example may help contrast the traditional problem solving program with the expert system approach. The example is the problem of tax advice. In the traditional approach data structures describe the taxpayer and tax tables, and a program in which there are statements representing an expert tax consultant's knowledge, such as statements which relate information about the taxpayer to tax table choices. It is this representation of the tax expert's knowledge that is difficult for the tax expert to understand or modify.

In the expert system approach, the information about taxpayers and tax computations is again found in data structures, but now the knowledge describing the relationships between them is encoded in data structures as well. The programs of an expert system are independent of the problem domain (taxes) and serve to process the data structures without regard to the nature of the problem area they describe. For example, there are programs to acquire the described data values through user interaction, programs to represent and process special organizations of description, and programs to process the declarations that represent semantic relationships within the problem domain and an algorithm to control the processing sequence and focus.

The general architecture of an expert system involves two principal components: a problem dependent set of data declarations called the knowledge base or rule base, and a problem independent (although highly data structure dependent) program which is called the inference engine. See more in How it Works chapter.

Individuals involved with expert systems
There are generally three individuals having an interaction with expert systems. Primary among these is the end-user; the individual who uses the system for its problem solving assistance. In the building and maintenance of the system there are two other roles: the problem domain expert who builds the knowledge base, and a knowledge engineer who assists the experts in determining the representation of their knowledge and who defines the inference technique required to obtain useful problem solving activity.

The end user
The end-user usually sees an expert system through an interactive dialog, an example of which follows:
 * Q. Do you know to which restaurant you want to go?
 * A. No
 * Q. Is there any kind of food you would particularly like?
 * A. Unknown
 * Q. Do you like spicy food?
 * A. No
 * Q. Do you usually drink wine with meals?
 * A. Yes
 * Q. When you drink wine, is it French wine?
 * A. Why

As can be seen from this dialog, the system is leading the user through a set of questions, the purpose of which is to determine a suitable set of restaurants to recommend. This dialog begins with the system asking if the user already knows the restaurant choice (a common feature of expert systems) and immediately illustrates a characteristic of expert systems; users may choose not to respond to any question. In expert systems, dialogs are not pre-planned. There is no fixed control structure. Dialogs are synthesized from the current information and the contents of the knowledge base. Because of this, not being able to supply the answer to a particular questions does not stop the consultation.

Another major distinction between expert systems and traditional systems is illustrated by the following answer given by the system when the user answers a question with the question "why", as occurred in the above example. The answer is:


 * A. I am trying to determine the type of restaurant to suggest. So far Chinese is not a likely choice. It is possible that French is a likely choice. I know that if the diner is a wine drinker, and the preferred wine is French, then there is strong evidence that the restaurant choice should include French.

It is very difficult to implement a general explanation system (answering questions like Why and How) in traditional systems. The response of the expert system to the question WHY is an exposure of the underlying knowledge structure. It is a rule; a set of antecedent conditions which, if true, allow the assertion of a consequent. The rule references values, and tests them against various constraints or asserts constraints onto them. This, in fact, is a significant part of the knowledge structure. There are values, which may be associated with some organizing entity. For example, the individual diner is an entity with various attributes (values) including whether they drink wine and the kind of wine. There are also rules, which associate the currently known values of some attributes with assertions that can be made about other attributes. It is the orderly processing of these rules that dictates the dialog itself.

The knowledge engineer
Knowledge engineers are concerned with the representation chosen for the expert's knowledge declarations and with the inference engine used to process that knowledge. There are several characteristics known to be appropriate to a good inference technique.


 * 1. A good inference technique is independent of the problem domain.


 * In order to realize the benefits of explanation, knowledge transparency, and reusability of the programs in a new problem domain, the inference engine must not contain domain specific expertise.


 * 2. Inference techniques may be specific to a particular task, such as diagnosis of hardware configuration. Other techniques may be committed only to a particular processing technique.


 * 3. Inference techniques are always specific to the knowledge structures.


 * 4. Successful examples of rule processing techniques include:


 * (a) Forward chaining


 * (b) Backward chaining

The inference rule
An understanding of the "inference rule" concept is important to understand expert systems. An inference rule is a statement that has two parts, an if-clause and a then-clause. An example of an inference rule is:


 * If the restaurant choice includes French, and the occasion is romantic,


 * Then the restaurant choice is definitely Paul Bocuse.

An expert system's rulebase is made up of many such inference rules. They are entered as separate rules and it is the inference engine that uses them together to draw conclusions. Because each rule is a unit, rules may be deleted or added without affecting other rules (though it should affect which conclusions are reached). One advantage of inference rules over traditional programming is that inference rules use reasoning which more closely resemble human reasoning.

Thus, when a conclusion is drawn, it is possible to understand how this conclusion was reached. Furthermore, because the expert system uses knowledge in a form similar to the expert, it may be easier to retrieve this information from the expert.

Chaining
There are two main methods of reasoning when using inference rules: backward chaining and forward chaining.

Forward chaining starts with the data available and uses the inference rules to conclude more data until a desired goal is reached. An inference engine using forward chaining searches the inference rules until it finds one in which the if-clause is known to be true. It then concludes the then-clause and adds this information to its data. It would continue to do this until a goal is reached. Because the data available determines which inference rules are used, this method is also called data driven.

Backward chaining starts with a list of goals and works backwards to see if there is data which will allow it to conclude any of these goals. An inference engine using backward chaining would search the inference rules until it finds one which has a then-clause that matches a desired goal. If the if-clause of that inference rule is not known to be true, then it is added to the list of goals. For example, suppose a rulebase contains two rules:


 * (1) If Fritz is green then Fritz is a frog.


 * (2) If Fritz is a frog then Fritz hops.

Suppose a goal is to conclude that Fritz hops.The rulebase would be searched and rule (2) would be selected because its conclusion (the then clause) matches the goal. It is not known that Fritz is a frog, so this "if" statement is added to the goal list. The rulebase is again searched and this time rule (1) is selected because its then clause matches the new goal just added to the list. This time, the if-clause (Fritz is green) is known to be true and the goal that Fritz hops is concluded. Because the list of goals determines which Rules are selected and used, this method is called goal driven.

Confidences
Another advantage of expert systems over traditional methods of programming is that they allow the use of confidences. When a human reasons he does not always conclude things with 100% confidence. He might say, "If Fritz is green, then he is probably a frog" (after all, he might be a chameleon). This type of reasoning can be imitated by using numeric values called Confidences. For example, if it is known that Fritz is green, it might be concluded with 0.85 Confidence that he is a frog; or, if it is known that he is a frog, it might be concluded with 0.95 Confidence that he hops. These numbers are similar in nature to probabilities, but they are not the same. They are meant to imitate the Confidences humans use in reasoning rather than to follow the mathematical definitions used in calculating probabilities.

The following general points about expert systems and their architecture have been illustrated.


 * 1. The sequence of steps taken to reach a conclusion is dynamically synthesized with each new case. It is not explicitly programmed when the system is built.


 * 2. Expert systems can process multiple values for any problem parameter. This permits more than one line of reasoning to be pursued and the results of incomplete (not fully determined) reasoning to be presented.


 * 3. Problem solving is accomplished by applying specific knowledge rather than specific technique. This is a key idea in expert systems technology. It reflects the belief that human experts do not process their knowledge differently from others, but they do possess different knowledge. With this philosophy, when one finds that their expert system does not produce the desired results, work begins to expand the knowledge base, not to re-program the procedures.

There are various expert systems in which a "rulebase" and an "inference engine" cooperate to simulate the reasoning process that a human expert pursues in analyzing a problem and arriving at a conclusion. In these systems, in order to simulate the human reasoning process, a vast amount of knowledge needed to be stored in the knowledge base. Generally, the knowledge base of such an expert system consisted of a relatively large number of "if then" type of statements that were interrelated in a manner that, in theory at least, resembled the sequence of mental steps that were involved in the human reasoning process.

Because of the need for large storage capacities and related programs to store the Rulebase, most expert systems have, in the past, been run only on large information handling systems. Recently, the storage capacity of personal computers has increased to a point where it is becoming possible to consider running some types of simple expert systems on personal computers.

In some applications of expert systems, the nature of the application and the amount of stored information necessary to simulate the human reasoning process for that application is just too vast to store in the active memory of a computer. In other applications of expert systems, the nature of the application is such that not all of the information is always needed in the reasoning process. An example of this latter type application would be the use of an expert system to diagnose a data processing system comprising many separate components, some of which are optional. When that type of expert system employs a single integrated Rulebase to diagnose the minimum system configuration of the data processing system, much of the Rulebase is not required since many of the components which are optional units of the system will not be present in the system. Nevertheless, earlier expert systems require the entire Rulebase to be stored since all the Rules were, in effect, chained or linked together by the structure of the Rulebase.

When the Rulebase is segmented, preferably into contextual segments or units, it is then possible to eliminate portions of the Rulebase containing data or knowledge that is not needed in a particular application. The segmenting of the Rulebase also allows the expert system to be run with systems or on systems having much smaller memory capacities than was possible with earlier arrangements since each segment of the Rulebase can be paged into and out of the system as needed. The segmenting of the Rulebase into contextual segments requires that the expert system manage various intersegment relationships as segments are paged into and out of memory during execution of the program. Since the system permits a Rulebase segment to be called and executed at any time during the processing of the first Rulebase, provision must be made to store the data that has been accumulated up to that point so that at some time later in the process, when the system returns to the first segment, it can proceed from the last point or RULE node that was processed. Also, provision must be made so that data that has been collected by the system up to that point can be passed to the second segment of the Rulebase after it has been paged into the system and data collected during the processing of the second segment can be passed to the first segment when the system returns to complete processing that segment.

The user interface and the procedure interface are two important functions in the information collection process.

The user interface
The function of the user interface is to present questions and information to the operator and supply the operator's responses to the inference engine.

Any values entered by the user must be received and interpreted by the user interface. Some responses are restricted to a set of possible legal answers, others are not. The user interface checks all responses to insure that they are of the correct data type. Any responses that are restricted to a legal set of answers are compared against these legal answers. Whenever the user enters an illegal answer, the user interface informs the user that his answer was invalid and prompts him to correct it. As explained in the cross referenced application, communication between the user interface and the Inference Engine is performed through the use of a User Interface Control Block (UICB) which is passed between the two.

Procedure node interface
The function of the Procedure node interface is to receive information from the Procedures coordinator and create the appropriate procedure call. The ability to call a procedure and receive information from that procedure can be viewed as simply a generalization of input from the external world. While in some earlier expert systems external information has been obtained, that information was obtained only in a predetermined manner so only certain information could actually be acquired. This expert system, disclosed in the cross-referenced application, through the knowledge base, is permitted to invoke any Procedure allowed on its host system. This makes the expert system useful in a much wider class of knowledge domains than if it had no external access or only limited external access.

In the area of machine diagnostics using expert systems, particularly self-diagnostic applications, it is not possible to conclude the current state of "health" of a machine without some information. The best source of information is the machine itself, for it contains much detailed information that could not reasonably be provided by the operator.

The knowledge that is represented in the system appears in the Rulebase. In the Rulebase described in the cross-referenced applications, there are basically four different types of objects, with associated information present.


 * 1. Classes--these are questions asked to the user.


 * 2. Parameters--a Parameter is a place holder for a character string which may be a variable that can be inserted into a Class question at the point in the question where the Parameter is positioned.


 * 3. Procedures--these are definitions of calls to external Procedures.


 * 4. Rule Nodes--The inferencing in the system is done by a tree structure which indicates the Rules or logic which mimics human reasoning. The nodes of these trees are called RULE nodes. There are several different types of RULE nodes.

The Rulebase comprises a forest of many trees. The top node of the tree is called the Goal node, in that it contains the conclusion. Each tree in the forest has a different Goal node. The leaves of the tree are also referred to as RULE nodes, or one of the types of RULE nodes. A leaf may be an EVIDENCE node, an EXTERNAL node, or a REFERENCE node.

An EVIDENCE node functions to obtain information from the operator by asking a specific question. In responding to a question presented by an EVIDENCE node, the operator is generally instructed to answer "yes" or "no" represented by numeric values 1 and 0 or provide a value of between 0 and 1, represented by a "maybe."

Questions which require a response from the operator other than yes or no or a value between 0 and 1 are handled in a different manner.

A leaf that is an EXTERNAL node indicates that data will be used which was obtained from a Procedure Call.

A REFERENCE node functions to refer to another tree or subtree.

A tree may also contain intermediate or minor nodes between the Goal node and the Leaf node. An intermediate node can represent logical operations like And or Or.

The inference logic has two functions. It selects a tree to trace and then it traces that tree. Once a tree has been selected, that tree is traced, depth-first, left to right.

The word "tracing" refers to the action the system takes as it traverses the tree, asking Classes (questions), calling Procedures, and calculating Confidences as it proceeds.

As explained in the cross-referenced applications, the selection of a tree depends on the ordering of the trees. The original ordering of the trees is the order in which they appear in the Rulebase. This order can be changed, however, by assigning an EVIDENCE node an attribute "initial" which is described in detail in these applications. The first action taken is to obtain values for all EVIDENCE nodes which have been assigned an "initial" attribute. Using only the answers to these initial Evidences, the Rules are ordered so that the most likely to succeed is evaluated first. The trees can be further re-ordered since they are constantly being updated as a selected tree is being traced.

It has been found that the type of information that is solicited by the system from the user by means of questions or classes should be tailored to the level of knowledge of the user. In many applications, the group of prospective uses is nicely defined and the knowledge level can be estimated so that the questions can be presented at a level which corresponds generally to the average user. However, in other applications, knowledge of the specific domain of the expert system might vary considerably among the group of prospective users.

One application where this is particularly true involves the use of an expert system, operating in a self-diagnostic mode on a personal computer to assist the operator of the personal computer to diagnose the cause of a fault or error in either the hardware or software. In general, asking the operator for information is the most straightforward way for the expert system to gather information assuming, of course, that the information is or should be within the operator's understanding. For example, in diagnosing a personal computer, the expert system must know the major functional components of the system. It could ask the operator, for instance, if the display is a monochrome or color display. The operator should, in all probability, be able to provide the correct answer 100% of the time. The expert system could, on the other hand, cause a test unit to be run to determine the type of display. The accuracy of the data collected by either approach in this instance probably would not be that different so the knowledge engineer could employ either approach without affecting the accuracy of the diagnosis. However, in many instances, because of the nature of the information being solicited, it is better to obtain the information from the system rather than asking the operator, because the accuracy of the data supplied by the operator is so low that the system could not effectively process it to a meaningful conclusion.

In many situations the information is already in the system, in a form of which permits the correct answer to a question to be obtained through a process of inductive or deductive reasoning. The data previously collected by the system could be answers provided by the user to less complex questions that were asked for a different reason or results returned from test units that were previously run.

How it works
Expert Systems consist of:
 * knowledge base (facts)
 * production rules ("if.., then..")
 * inference engine (controls how "if.., then.." rules are applied towards facts)

Actually there are two methods to make conclusions. Nice & simple tutorial about backward and forward chaining

Prominent expert systems

 * Dendral
 * Dipmeter Advisor
 * Mycin
 * CADUCEUS (expert system)
 * R1 (expert system)
 * CLIPS, Jess (java adopted CLIPS syntax)
 * Prolog