Expert system: Encyclopedia II - Expert system - Confidences

Expert system - 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.

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.

Expert system - 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.

Expert system - 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.

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.!

Adapted from the Wikipedia article "Confidences", under the G.N U Free Docmentation License. Please also see http://en.wikipedia.org/wiki