12-10-2013_09-24-50 / [Simon_Kendal,_Malcolm_Creen]_Introduction_to_Know(BookFi.org)

As well as considering the type of interview to be performed the knowledge engineer has other issues to consider including:

how to document the knowledge acquisition process

issues relating to the use of multiple experts.

Documenting the Knowledge Acquisition Process

Forms are used to broadly itemise the aims of the session, and can be given to an expert in advance of the session, to be used as an agenda. The forms can be completed during the interview as agreed by both parties.

Recording the results of knowledge acquisition process is important, as this allows the knowledge bases that are ultimately developed to be validated. If the correct paperwork is followed, it should be possible after a system has been developed to find details of the interview that was the source of an item of knowledge. Knowing this it will be possible to go back and question the relevant expert about the validity of that knowledge.

Dealing with Multiple Experts

In this situation, more than one expert is interviewed to obtain knowledge about a specific domain.

Knowledge acquisition involving multiple experts involves three distinct difficulties:

the problems of dealing with experts individually, plus

the problems of coordinating human interactions – especially if time is short

the obstacles and risks of trying to integrate different knowledge and different methods of solving problems.

Clearly, using multiple experts can be difficult, and can involve obtaining conflicting knowledge. The experts may use different reasoning techniques and different processes to solve the problems, and these may not always be easily merged or linked together. If multiple experts are required and it is found that they use conflicting knowledge the feasibility of developing the expert system should be questioned unless the experts themselves can resolve their conflicting problem-solving approaches.

Despite the difficulties using multiple experts it is possible to iron out areas of uncertainty or conflicting knowledge, and to use one expert as a validation tool. This allows a higher quality knowledge base to be developed—potentially better than any one individual expert.

Repertory grids can be used as a simple method to represent the opinions of several experts. Each expert is asked to provide a ranked estimate and the values in each element are simply averaged.

The modes of transport available are:

walk

cycle

skateboard

car

train

bus

aeroplane

tram

taxi

motor-bike.

Place these modes of travel in a repertory grid and choose three pairs of factors that will help to differentiate people’s choices between the different modes.

Question 2

Investigate some of the repertory grid analysis tools by visiting the website below and following the links to the grids themselves.

http://www.psyctc.org/grids/grids.html

Question 3

In which type of interview (unstructured, structured and event recall) would you expect to find the following questions:

1.Starting from the first day of your last holiday, until the final day, please tell me everything that happened.

2.How do you play chess?

3.If the traffic light is on red should I start the car or stop it?

Question 4

For the following two problems identify which types of interview would be appropriate:

1.A knowledge engineer wishes to become familiar with a new subject area.

2.A knowledge engineer wishes to complete and check the knowledge obtained from earlier knowledge acquisition sessions.

Answer to Self-Assessment Questions

Answer 1

Suggestions for factors are given below; however, there are many possible factors that can be used.

Answer 2

No specific solution.

Answer 3

1.This would be appropriate in an event recall interview that is used to obtain sequencing information and to check whether it matches knowledge collected previously via other interview techniques. We may need to consider the entire sequence activities starting at the very beginning and finishing at the very end of the event. In this example, for instance, it may be necessary to consider the actions leading up to the holiday from the first time that the holiday was suggested, to the choice of destination and the preparations that were made in order to go on the holiday. The post holiday sequence of actions may also be important—how you got home, unpacked, etc.

2.This sort of question would be appropriate in an unstructured interview. Questions in this type of interview are often short and the answers are often long. While this type of interview can often be time consuming, unstructured interviews are essential as they allow the expert to bring important issues to the attention of the knowledge engineer.

3.This sort of question is typical in a structured interview where the knowledge engineer is looking for specific information. The questions are often long and the answers relatively short. As the knowledge engineer is looking for specific information the questions need to be prepared in advance of the interview. Structured interviews are good for filling in the gaps left from earlier interviews.

Answer 4

1.The two most appropriate methods here are tutorial interviews and unstructured interviews. However, it is worth remembering that even before conducting these interviews the knowledge engineer should consider doing preliminary bookwork or talking to other people in order to learn some of the terminology.

2.This problem really has two parts: how to complete the knowledge acquisition process and how to check it.

Structured interviews can be used to plug any known gaps in the knowledge but what about unknown gaps? By conducting event recall interviews, particularly

where difficult or unusual events are considered, issues will arise that were glossed over by the expert or missed by the knowledge engineer. If no such issues arise we can have some confidence that the knowledge obtained is complete.

Event recall interviews are also a useful method of checking the knowledge obtained. Does the procedure followed during a real event actually match the procedure as described in earlier knowledge acquisition sessions? The other interview technique that is most obviously useful for the task of checking the knowledge is the teach back interview—where the knowledge engineer prepares a presentation to the expert and the expert verifies they understand the subject correctly. There is one other very important method of checking the knowledge obtained. If a prototype expert system is created, incorporating the knowledge obtained, the quality of the knowledge in the expert system can be demonstrated by testing the reasoning powers of system developed.

SECTION 1: USING KNOWLEDGE

Introduction

This section introduces the subject of knowledge representation. It briefly looks at the use of rules and declarative programming. Rule-based approaches to knowledge representation are covered in depth in section 2 of this chapter.

Objectives

By the end of the section you will be able to:

explain how knowledge can be represented in declarative programmes

distinguish between shallow and deep knowledge in the context of ESs.

Different Types of Knowledge

Acquiring knowledge is one of the main objectives of humans. We have always sought to obtain knowledge, to apply that knowledge to solve everyday problems, and to expand on it to improve ourselves and our environment.

However, while some knowledge is easy to obtain and understand (e.g. what the sequence and meaning of traffic lights is), other knowledge may be difficult to obtain or interpret. For example, many of the problems being tackled by experts are poorly understood because the subject areas are very specialised and experts find it difficult to communication information on that knowledge area.

In many situations, experts do not have any formal basis for problem solving or communicating the results of that problem solving. So they tend to use ‘rules of thumb’ (heuristics) developed on the basis of their experience to help them make decisions in their particular field.

Heuristics implies that knowledge is acquired more by trial and error than by any definite decision-making process. This does not cast doubt on the value of heuristics: the rules established by experts through experience can often be valuable and this differentiates the ‘expert’ from less experienced people. An example of a heuristic is the statement that people with high blood pressure are more likely to have a heart attack. While not all people with high blood pressure have a heart attack it is still a useful rule of thumb and if a doctor advised you that it was critical to lower your blood pressure you would be unlikely to ignore this advice.

Deep and Shallow Knowledge

Expert systems, can have two types of knowledge: deep and shallow.

Shallow knowledge indicates that the system has been built with a working knowledge of the problem domain only and a system built on the basis of it will only be useful in some very specific situations. If the problem facing the ES is not covered by any of these basic relationships, then the system will not be able to provide a solution to that problem.

Deep knowledge is knowledge of the fundamental laws on which a system is based. This knowledge is not dependent on a specific situation and can thus be used to solve a range of problems. As with a human expert, the system will be able to ‘understand’ the causal links within the knowledge base. Including this type of knowledge within an ES is difficult, if not impossible, in many situations. Many systems are simply too complex to show all the causal relationships, and experts may sometimes make intuitive leaps to reach a conclusion, which the ES cannot mimic.

Shallow knowledge therefore tends to be:

task dependent

brittle

additive

but also provides effective reasoning.

On the other hand, deep knowledge tends to be

task independent

describes causal relations

complete at a certain level of abstraction

however reasoning with this knowledge can be difficult.

Activity 1

An ES has been produced to provide assistance in the diagnosis of faults in Brand X televisions.

Explain how the concepts of shallow and deep knowledge could be applied to this system.

Comment on how difficult deep knowledge is to apply in such a context.

Feedback 1

Shallow knowledge implies that the ES only has information concerning its domain—perhaps a database of common faults and probable causes and solutions for this particular brand of television. Using this knowledge the system will provide correct fault-finding diagnosis on most occasions.

However, this knowledge would not apply to fixing faults in TVs made by other manufacturers nor to other types of TV receivers such as plasma screen TVs if company X started to make these.

Deep knowledge could be built into the system, such as the relationship between voltage, resistance and current (Ohm’s law) and other aspects of the physics of electricity. This additional knowledge might allow the ES to diagnose faults in other types of electrical equipment. However, such diagnosis would be working from first principles and reaching a diagnosis could be difficult and time consuming. Deep knowledge is therefore more difficult to apply because it is not based on specific knowledge of a particular piece of equipment that may be gained from experience.

Representing Rules in ESs

Most decision-making rules within ESs can be represented using the IF. . . THEN format, that is

IF situation THEN action

Other clauses such as OR and ELSE can also be used within this construct to show alternative situations or different courses of action.

For example, a simple rule could be:

IF Christmas day falls on a Monday, OR Christmas day falls on a Tuesday

THEN many factories will close for the whole of Christmas week.

Features of Rule-Based Systems

The main features of rule-based systems are:

They represent practical human reasoning in the form of IF. . . THEN rules. Most humans use this type of statement (even if they don’t realise it) so the syntax will be relatively easy to understand.

The knowledge of the system grows as more rules are added. This implies that the accuracy of the predictions made by the system will increase as more rules become available.

Where a conflict occurs between two or more rules, the rule-based system may be sophisticated enough to try to choose the best rule to use.

Use an inference technique (either backward or forward chaining—explained later in this chapter) to manipulate expertise to solve a problem.

Procedural vs. Declarative Programming

Procedural Programming

A program written in C++ or Java consists of a set of procedures that must be performed in a strict sequence to accomplish a purpose. This is procedural programming.

Key features of procedural programming include:

The programme is constructed as a sequence of step-by-step ‘how to’ instructions. However, explanations may be included to tell the user why certain activities are being carried out.

The programming format implies automatic response to stimuli; there is little or no thinking about the response or course of action, these are included within the programme.

Declarative Programming

Rules in a KBS stand alone as statements of truth or fact and can be used by an inference engine to reach other true conclusions. This is declarative programming. The sequence of actions taken during processing is not defined. The inference engine will automatically select and apply rules as it sees fit.

Key features of declarative programming are listed below:

It provides facts on a given knowledge domain, literally stating what things are.

The statements within declarative programming provide information concerning the associations between different objects.

Knowledge is normally shallow or surface-level, providing examples of associations that experts can visualise easily.

Activity 2

Explain what type of programming each of the following statements are examples of:

1.Smoking causes cancer.

2.Find the price of a new car. If the price is less than £10,000, then purchase the car, else leave money in the bank and check prices in another month.

Feedback 2

Statement 1 is an example of declarative knowledge because it provides a statement about knowledge within a domain.

Statement 2 is an example of procedural knowledge because it provides a list of instructions to carry out.