Expert Systems

Question 1

What is an expert system?

Answer 1

Computerised system that attempts to reproduce the decision-making process of an expert human being.

Question 2

Who designs expert systems?

Answer 2

Expert system engineers

Question 3

What is its main goal?

Answer 3

Replicate the judgement of a human that has expert knowledge in a certain field to replace or assist a human expert.

Question 4

Components

Answer 4

• A user interface
• A knowledge base (+rules base)
• An inference engine
• A knowledge base editor
• An explanation system
• Method of output, e.g. screen

Question 5

How does an e.s. operate?

Answer 5

Prompts the user to enter certain data using the user interface, referring to the knowledge base and using the inference engine to aid the decision-making process it is designed to simulate.

Question 6

Examples of e.s. usages

Answer 6

• Diagnostic tool
• Financial planning
• Risk analysis

Question 7

What’s the UI?

Answer 7

Way a user interacts with the expert system. This could include using a keyboard to enter criteria into text query boxes, or choosing options by pressing offered choices on a touch screen.

It will guide the user about what data they need to input into the expert system and will then display any output from the system.

Question 8

What would the user need to do w/o the presence of a user interface?

Answer 8

Program each of the interactions they want to make with the expert system

Question 9

What’s the knowledge base?

Answer 9

Database of related information about a particular subject. It allows the storage and retrieval of the knowledge required for an expert system to operate.

Question 10

How is a knowledge base made?

Answer 10

Several experts will be interviewed and asked to contribute the knowledge they have of a given field.
It uses factual knowledge (definitive and widely shared amongst experts) and heuristic knowledge (acquired through personal experience and built on reasoning).

Question 11

What’s another part of the knowledge base?

Answer 11

Rules base. Set of rules that will be used to produce an output by the expert system. These rules are used by the inference engine as a base for reasoning, to obtain a solution to a problem or a decision.

Question 12

How are the rules for the rules based made?

Answer 12

Each rule will contain two parts, the IF and the THEN. A rule can also have multiple IF parts that will be joined together by Boolean operators including AND and OR.

For example, IF a > b AND a > c THEN highest = a

Question 13

What’s the 3rd component for the knowledge base?

Answer 13

Knowledge base editor. Allows the knowledge base to be edited and updated when necessary, since it should be a dynamic resource, therefore the expert system needs to have a knowledge base editor.

Question 14

What’s the inference engine?

Answer 14

Part of the expert system that makes judgements and reasoning using the knowledge base and user responses until it reaches a decision. It is designed to produce reasoning based on the rules and knowledge base.

Question 15

Why is the inference engine considered a problem solving tool?

Answer 15

It organises and controls the steps to providing the desired output.

Question 16

What are two main methods that an inference engine can use to simulate reasoning?

Answer 16

• Backward chaining
• Forward chaining

Question 17

What type of reasoning is Backward chaining?

Answer 17

Goal driven reasoning (dependent on finding a desired goal).

Question 18

How does backward chaining work?

Answer 18

The system tries to take a goal and repeatedly split it into sub-goals that are simpler to achieve to inform the next piece of data that is needed by the system, to reach a goal.

In backward chaining, the inference system knows the final decision, it tries to figure out the conditions that would have resulted in that decision. It is mostly used in finding the cause of a problem.

This strategy is used to answer the question “WHY DID THIS HAPPEN?”

Question 18

When is backward chaining used?

Answer 18

When the possible outcomes are limited and definitive in nature.

Question 18

Examples of backward chaining usages.

Answer 18

Debugging, diagnostics, and prescription applications.

Question 18

What type of reasoning is Forward chaining?

Answer 18

Data driven reasoning (dependent on the data that it is provided with).

Question 19

When is forward chaining used?

Answer 19

When a problem is more open ended, and the outcome is not necessarily definitive in nature.

Question 19

How does forward chaining work?

Answer 19

Take the data input by a user, then move from rule to rule to find one where the clause for the data input is true. It will then ask the user for more data and repeat this process until it can suggest an outcome. It goes through all the facts, conditions, and derivations before deducing the outcome.

Question 19

What’s the method of output?

Answer 19

Way the user will use to view any results produced by the expert system. This will often be in the form of a display screen that will allow them to see the results on screen, or may include an output, such as a printer, that allows the results to be printed and viewed.

Question 19

Examples of forward chaining usage.

Answer 19

Planning, monitoring, controlling, and interpreting applications.

Question 19

What’s the purpose of an explaination system?

Answer 19

Provide an explanation of the reasoning process and show how the output given by the system was achieved.
The conclusion or decision the expert system provides may not always be an obvious choice to a user. The user may want to gain an understanding of how the conclusion or decision was determined.
In order to allow this facility, some expert systems have an explanation system built into them.