Rule – Based Expert System

Expert System

First, What is Expert System. An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted. Expert systems are most common in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence. A wide variety of methods can be used to simulate the performance of the expert however common to most or all are 1) the creation of a so-called "knowledgebase" which uses some knowledge representation formalism to capture the Subject Matter Expert's (SME) knowledge and 2) a process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering. Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or a supplement to some information system.

As a premiere application of computing and artificial intelligence, the topic of expert systems has many points of contact with general systems theory, operations research, business process reengineering and various topics in applied mathematics and management science.

Expert systems are designed and created to facilitate tasks in the fields of accounting, medicine, process control, financial service, production, human resources etc. Indeed, the foundation of a successful expert system depends on a series of technical procedures and development that may be designed by certain technicians and related experts.

Rule – Based Expert System

A rule-based expert system is an expert system which works as a production system in which rules encode expert knowledge.

Most expert systems are rule-based. Alternatives are :

1. frame-based - knowledge is associated with the objects of interest and reasoning consists of confirming expectations for slot values. Such systems often include rules too. 

2. model-based, where the entire system models the real world, and this deep knowledge is used to e.g. diagnose equipment malfunctions, by comparing model predicted outcomes with actual observed outcomes

3. case-based - previous examples (cases) of the task and its solution are stored. To solve a new problem the closest matching case is retrieved, and its solution or an adaptation of it is proposed as the solution to the new problem.

Data-driven Rule-based Expert Systems

Use Forward Chaining:

Given a certain set of facts in WM, use the rules to generate new facts until the desired goal is reached.

To forward chain the inference engine must:

1. Match the condition patterns of rules against facts in working memory.

2. If there is more than one rule that could be used (that could "fire"), select which one to apply (this is called conflict resolution)

3. Apply the rule, maybe causing new facts to be added to working memory

4. Halt when some useful (or goal) conclusion is added to WM (or until all possible conclusions have been drawn.)

Sources :

Wikipedia (http://en.wikipedia.org/wiki/Expert_system)

cs.tcd.ie (https://www.cs.tcd.ie/Lucy.Hederman/DipHI/RuleBasedExpSys.doc)