1. Knowledge-Based Systems
Unit V Chapter 1
Knowledge-Based Systems

2. Structure of an Expert System
The internal structure of an expert system can be considered to consist of three parts:
The knowledge base ; the database; the rule interpreter.

3. The knowledge base holds the set of rules of inference that are used in reasoning. Most of these systems use IF-THEN rules to represent knowledge. Typically systems can have from a few hundred to a few thousand rules.
The database gives the context of the problem domain and is generally considered to be a set of useful facts. These are the facts that satisfy the condition part of the condition action rules as the IF THEN rules can be thought of.
The rule interpreter is often known as an inference engine and controls the knowledge base using the set of facts to produce even more facts. Communication with the system is ideally provided by a natural language interface. This enables a user to interact independently of the expert with the intelligent system.

4. Expert Systems in different Areas
Diagnosis and Troubleshooting of Devices and Systems of All Kinds
Planning and Scheduling
Financial Decision Making
Knowledge Publishing
Process Monitoring and Control
Design and Manufacturing

5. Examples of Expert Systems
1. MYCIN:
Historically, the MYCIN system played a major role in stimulating research interest in rule-based expert systems.
MYCIN is an expert system for diagnosing and recommending treatment of bacterial infections of the blood (such as meningitis and bacteremia). 

6. 2. Dendral
Dendral is one of the great classic application programs. To see what it does, let us suppose that an organic chemist wants to know the chemical nature of some substance newly created in the test tube. The first step is to determine the number of atoms of various kinds in one molecule of the stuff. This step determines the chemical formula, such as C8H16O. The notation indicates that each molecule has eight atoms of carbon, 16 of hydrogen, and one of oxygen.

7. 3.  XCON:
Historically, the XCON system played a major role in stimulating commercial interest in rule based expert systems. XCON’s domain is computer-system components. When a company buys a big computer (main frame) it buys a central processor, memory, terminals, disk drives, tape drives, various peripheral controllers and other paraphernalia. All these components ( in number) must be arranged sensibly along input-output buses. Moreover all the electronic modules must be placed on the proper kind of cabinet in a suitable slot of suitable back plane.

8. 4. ACE
ACE, a system for Automated Cable Expertise, is a Knowledge-Based Expert System designed to provide trouble-shooting reports and management analyses for telephone cable maintenance in a timely manner.

9. LISP(List Processing)
John McCarthy invented LISP in 1958, shortly after the development of FORTRAN. It was first implemented by Steve Russell on an IBM 704 computer.
It is particularly suitable for Artificial Intelligence programs, as it processes symbolic information effectively.

10. Features of Common LISP
It is machine-independent
It uses iterative design methodology, and easy extensibility.
It allows updating the programs dynamically.
It provides high level debugging.
It provides advanced object-oriented programming.
It provides a convenient macro system.
It provides wide-ranging data types like, objects, structures, lists, vectors, adjustable arrays, hash-tables, and symbols.
It is expression-based.
It provides an object-oriented condition system.
It provides a complete I/O library.
It provides extensive control structures.

11. Expert System Shells
An Expert system shell is a software development environment. It contains the basic components of expert systems. A shell is associated with a prescribed method for building applications by configuring and instantiating these components.

12. Shell components and description
The generic components of a shell : the knowledge acquisition, the knowledge Base, the reasoning, the explanation and the user interface are shown below. The knowledge base and reasoning engine are the core components.

13. Knowledge Base
A store of factual and heuristic knowledge. Expert system tool provides one or more knowledge representation schemes for expressing knowledge about the application domain. Some tools use both Frames (objects) and IF-THEN rules. In PROLOG the knowledge is represented as logical statements.

14. Reasoning Engine
Inference mechanisms for manipulating the symbolic information and knowledge in the knowledge base form a line of reasoning in solving a problem. The inference mechanism can range from simple modus ponens backward chaining of IF-THEN rules to Case-Based reasoning.

15. Knowledge Acquisition subsystem
A subsystem to help experts in build knowledge bases. However, collecting knowledge, needed to solve problems and build the knowledge base, is the biggest bottleneck in building expert systems.

16. Explanation subsystem
A subsystem that explains the system's actions. The explanation can range from how the final or intermediate solutions were arrived at justifying the need for additional data.

17. User Interface
A means of communication with the user. The user interface is generally not a part of the expert system technology. It was not given much attention in the past. However, the user interface can make a critical difference in the pe eived utility of an Expert system.

18. What is Knowledge Engineering?
Imagine an education company wanting to automate the teaching of children in subjects from biology to computer science (requiring to capture the knowledge of teachers and subject matter experts) or Oncologists choosing the best treatment for their patients (requiring expertise and knowledge from information contained in medical journals, textbooks, and drug databases).

19. Knowledge Engineering is the process of imitating how a human expert in a specific domain would act and take decisions. It looks at the metadata (information about a data object that describes characteristics such as content, quality, and format), structure and processes that are the basis of how a decision is made or conclusion reached.

20. Knowledge Engineering Environment
Knowledge Engineering Environment (KEE) is a frame-based development tool for expert systems.
It was developed and sold by IntelliCorp, and first released in 1983.
On KEE, several extensions were offered:
Simkit, a frame-based simulation library.
KEEconnection, database connection between the frame system and relational databases.

21. KEE provides an extensive graphical user interface (GUI) to create, browse, and manipulate frames.
KEE also includes a frame-based rule system. In the KEE knowledge base, rules are frames. Both forward chaining and backward chaining inference are available.

22. Knowledge Engineering system
In terms of its role in artificial intelligence (AI), knowledge engineering is the process of understanding and then representing human knowledge in data structures, semantic models (conceptual diagram of the data as it relates to the real world) and heuristics (rules that lead to solution to every problem taken in AI). Expert systems, and algorithms are examples that form the basis of the representation and application of this knowledge.
The knowledge engineering process includes:
Knowledge acquisition
Knowledge representation
Knowledge validation
Inferencing
Explanation and justification

24. Web-based expert system
Web-Based ES Application
The Internet offers an ever-expanding set of capabilities and Web-based ES is capable of offering much more than traditional ES.

25. Example
Fish-Expert
Fish disease diagnosis is a rather complicated process in aquaculture production activities. Fish-Expert is a Web-based expert system for fish disease diagnosis in China. This Web-based expert system can mimic human fish disease expertise and diagnose a number of fish diseases with a user-friendly interface. A fish disease diagnosis expert system contains a large amount of fish disease data and images, which are used to conduct online disease diagnosis. 

26. A number of points related to the benefits and challenges of Web-based ES emerged with the development and use of Fish-Expert, and these include:
• Online knowledge acquisition is welcomed by domain experts, but a knowledge engineer is still needed to check and transfer the knowledge into the knowledge base.
• The online user feedback form and online evaluation of ES seem to be effective and popular.
• Internet access speed is seen as a bottleneck for Web-based ES applications, especially in developing countries.
• The multimedia interface in Fish-Expert is effective in helping the user to query the system, but it slows down the access speed to it.
• ES are known for their inability to deal with exceptions or complex problems due to the inflexibility and limits of the knowledge base.