expert systems

F29IF2 : Databases & Information Systems Lachlan M. MacKinnon

Expert Systems

Databases & Information Systems

Lachlan M. MacKinnon


What is an Expert System?“..an intelligent computer program that

uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution.” (Feigenbaum 1982)

The area of expert systems is a very successful approximate solution to the classic AI problem of programming intelligence.

Thus, an expert system emulates the decision-making ability of a human expert.


There is no general purpose problem solver yet developed, but expert systems function well in restricted domains.

Initially, expert systems were specifically those which contained expert knowledge, obtained from human experts. The term now covers any system which uses expert system technology (e.g. languages, programs, or hardware designed to aid in the development and execution of expert systems.)

The knowledge contained in an expert system can be expertise, public knowledge, domain


or specialised task knowledge, and may be obtained from public or private media, as well as from knowledgeable persons or experts.

Thus the terms expert system, knowledge-based system and knowledge-based expert system, are often used synonymously.

Knowledge-base

Inference Engine

User Expertis

e

Facts

Expert System


Basic concept of expert system :• User supplies facts or other information to

the expert system and receives expert advice or expertise in response

• Internally, the expert system consists of two main components :

– the knowledge-base contains the knowledge– the inference engine draws conclusions from the

knowledge

• The expert knowledge is specific to a problem domain, i.e. medicine, finance, science, etc. However, within the problem domain there is the knowledge domain of the expert or system, which is a wholly contained subset of the problem domain


• For example, an expert system containing knowledge about electronic engineering might not know anything about power engineering, even though the problem domain could be identified as electrical engineering.

• In the knowledge domain, an expert system reasons or makes inferences in the same way that a human expert would infer the solution of a problem.

• Expert systems can be used as replacements for human experts, in situations where the problem domain is small and well-defined and the knowledge domain of the system is equivalent. However, they are more widely used as intelligent assistants, i.e. decision support systems


Advantages of Expert Systems• Increased availability

– expertise becomes available on any suitable computer hardware, thus the system disseminates expertise more widely

• Reduced Cost – cost per user of providing expertise is lowered

• Reduced Danger– expert systems can be used in situations that

would be hazardous to a human

• Permanence– human experts are impermanent

• Multiple expertise– can include the expertise of several human

experts


• Increased Reliability– not subject to human variability– can be used to confirm or increase confidence

that correct decision has been reached. Not advisable if human expert being assisted by system was the one who designed the system

• Explanation– system can explicitly explain in detail to all

interested parties, at all times, the reasoning that leads to a conclusion. This increases confidence in the decision, and a human expert would be unlikely to have the time, or the patience, to do this.

• Fast Response– For some applications, especially real-time

systems, the expert system may respond faster and be more available than the human expert.


• Steady, unemotional and complete response at all times

– expert systems don’t suffer from stress or fatigue(??!!)

• Intelligent Tutor– the ability to test sample scenarios and provide

detailed reasoning for decisions makes the expert system a useful tool for tutoring, especially in specialist domains.

• Intelligent Database– expert systems can be used to access data from a

database relative to some problem solution strategy developed by the system.

• Explicating Expert Knowledge– the knowledge of human experts must be put in an

explicit form to be entered in the computer, enabling it to be examined for correctness, consistency & completeness


General Concepts of Expert Systems• Knowledge represented by rules (i.e. if-then)• Systems such as CLIPS also permit objects• Building the system is called knowledge

engineering• Sophisticated systems contain explanation

facilities, possibly even permitting multiple “What-if” style questions - hypothetical reasoning

• Some systems permit rule induction, in which the system creates rules from tables of data.

• Expert systems should be able, as a human expert, to degrade advice gracefully as they reach their limits of ignorance


• Expert systems are limited by lack of causal knowledge, they do not have knowledge of the underlying causes and effects in a system.

• Thus, it is much easier to build expert systems with shallow knowledge based on empirical and heuristic knowledge than deep knowledge based on the basic structure, function and behaviour of objects.

•Heuristic knowledge– rules of thumb– experience-based empirical knowledge– short cuts saving time and cost

• Expert systems are also limited by their inability to generalise their knowledge by analogy


Broad Classes of Expert Systems• Configuration

– assemble proper components of system in proper way

• Diagnosis– infer underlying problems based on observed

evidence

• Instruction• Interpretation• Monitoring• Planning• Prognosis• Remedy• Control