STRUCTURE OF EXPERT SYSTEMS

10.7 STRUCTURE OF EXPERT SYSTEMS

Expert systems can be viewed as having two environments: the development environ- ment and the consultation (runtime) environment (Figure 10.3). The d e v e l o p m e n t

C H A P T E R 1 0 ARTIFICIAL INTELLIGENCE A N D EXPERT SYSTEMS: K N O W L E D G E - B A S E D SYSTEMS

F I G U R E 1 0 . 3 STRUCTURE O F A N EXPERT SYSTEM

the knowledge base. The consultation environment is used by a non-expert to obtain expert knowledge and advice. These environments can be separated once a system is

completed. • •- " . . . ^ • The three major components that appear in virtually every expert system are the knowledge base, inference engine, and user interface. A n expert system that interacts with users may also contain the following additional components:

• Knowledge acquisition subsystem • Blackboard (workplace) • Explanation subsystem (justifier) • Knowledge-refining system

Most expert systems do not at present contain the knowledge-refinement compo- nent. A brief description of each component follows.

KNOWLEDGE ACQUISITION SUBSYSTEM Knowledge acquisition is the accumulation, transfer, and transformation of problem-

solving expertise from experts or documented knowledge sources to a computer pro- gram for constructing or expanding the knowledge base. Potential sources of knowl-

S 5 8 P A R T I V INTELLIGENT DECISION SUPPORT SYSTEMS

Acquiring k n o w l e d g e from experts is a complex task that o f t e n creates a bottle- neck in E S construction. In building large systems o n e needs a knowledge engineer

or k n o w l e d g e elicitation expert to interact with o n e or m o r e h u m a n e x p e r t s in building the k n o w l e d g e base. Typically the k n o w l e d g e e n g i n e e r helps the expert structure the problem area by interpreting and integrating human answers to ques- tions, drawing analogies, posing counterexamples, and bringing conceptual difficul- ties to light.

KNOWLEDGE BASE The knowledge base contains the relevant knowledge necessary for understanding, for-

mulating, and solving problems. It includes two basic elements: (1) facts, such as the problem situation and the theory of the problem area, and (2) special heuristics or rules that direct the use of k n o w l e d g e to solve specific problems in a particular domain. (In addition, the inference engine can include general purpose problem- solving and decision-making rules.) The heuristics express the informal judgmental knowledge in an application area. Knowledge, not mere facts, is the primary raw mate-

rial of expert systems.

INFERENCE ENGINE The "brain" of the E S is the inference engine, also known as the control structure or

the rule interpreter (in rule-based ES).This component is essentially a computer pro- gram that provides a methodology for reasoning about information in the knowledge base and on the blackboard, and for formulating conclusions. This component provides directions about how to use the system's knowledge by developing the agenda that organizes and controls the steps taken to solve problems whenever consultation takes place. It will be further elaborated in Section 10.8.

USER INTERFACE Expert systems contain a language processor for friendly, problem-oriented communi-

cation between the user and the computer. This communication can best be carried out in a natural language. D u e to technological constraints, most existing systems use the question-and-answer approach to interact with the user. Sometimes it is supplemented by menus, electronic forms, and graphics.

BLACKBOARD (WORKPLACE) The blackboard is an area of working memory set aside as a database for the descrip-

tion of a current problem as specified by the input data; it is also used for recording intermediate hypotheses and decisions. Three types of decisions can be recorded on the blackboard: a plan ( h o w to attack the problem), an agenda (potential actions awaiting execution), and a solution (candidate hypotheses and alternative courses of action that the system has generated thus far).

Consider an example. When your car fails, you enter the symptoms of the failure into the computer for storage in the blackboard. As the result of an intermediate hypothesis developed in the blackboard, the computer may then suggest that you do

C H A P T E R 10 ARTIFICIAL INTELLIGENCE A N D EXPERT SYSTEMS: K N O W L E D G E - B A S E D SYSTEMS

EXPLANATION SUBSYSTEM (JUST1FIER) The ability to trace responsibility for conclusions to their sources is crucial both in the

transfer of expertise and in problem-solving. The explanation subsystem (also called Justifier) can trace such responsibility and explain the E S behavior by interactively

answering questions like the following: • Why was a certain question asked by the expert system?

• H o w was a certain conclusion reached? • Why was a certain alternative rejected? • What is the plan to reach the solution? For example, what remains to be estab-

lished before a final diagnosis can be determined? In simple ES, the explanation shows the rules used to derive the specific recom-

mendations.