Page 243 - DCAP310_INTRODUCTION_TO_ARTIFICIAL_INTELLIGENCE_AND_EXPERT_SYSTEMS

Unit 13: Expert System Architecture

The user interface can be judged by how well it reproduces the kind of interaction one might Notes
expect between a human expert and someone consulting that expert.

Knowledge Base

The knowledge base consists of specific knowledge about some substantive domain. A knowledge
base differs from a data base in that the knowledge base includes both explicit knowledge and
implicit knowledge. Much of the knowledge in the knowledge base is not stated explicitly, but
inferred by the inference engine from explicit statements in the knowledge base. This makes
knowledge bases have more efficient data storage than data bases and gives them the power to
exhaustively represent all the knowledge implied by explicit statements of knowledge.

Knowledge bases can contain many different types of knowledge and the process of acquiring
knowledge for the knowledge base (this is often called knowledge acquisition) often needs to be
quite different depending on the type of knowledge sought.

Types of Knowledge

There are many different kinds of knowledge considered in expert systems. Many of these form
dimensions of contrasting knowledge:
Explicit knowledge

Implicit knowledge
Domain knowledge
Common sense or world knowledge
Heuristics
Algorithms
Procedural knowledge

Declarative or semantic knowledge
Public knowledge
Private knowledge
Shallow knowledge
Deep knowledge
Meta knowledge

Another View of Expert System Architecture

Following figure shows the most important modules that make up a rule-based expert system.
The user interacts with the system through a user interface which may use menus, natural language
or any other style of interaction). Then an inference engine is used to reason with both the expert
knowledge (extracted from our friendly expert) and data specific to the particular problem being
solved. The expert knowledge will typically be in the form of a set of IF-THEN rules. The case
specific data includes both data provided by the user and partial conclusions (along with certainty
measures) based on this data. In a simple forward chaining rule-based system the case specific
data will be the elements in working memory.

Almost all expert systems also have an explanation subsystem, which allows the program to
explain its reasoning to the user. Some systems also have a knowledge base editor which help the
expert or knowledge engineer to easily update and check the knowledge base.

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