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

to be pre-processed to identify relevant peaks in the spectra. This information was then presented to the expert system in a form it could understand.

This situation highlights one important issue when choosing which AI technology to apply to a given problem. Usually there is more than one way of solving a problem. While choosing the ‘best’ technology may offer advantages, and make the task easier, business considerations may require the use of an alternative technology.

Further information on condition monitoring can be found at the following websites:

http://www.maintenanceworld.com/preventive-maintenance.html http://www.pemms.co.uk/Condition Monitoring.html http://www.plant-maintenance.com/articles/ConMon21stCentury.shtml http://www.engineeringtalk.com/guides/condition-monitoring.html.

Current Research in NNs

The following web addresses contain information on research in NNs:

IEEE Computational Intelligence Society at: http://ieee-cis.org/

Neural Networks Group—University of Edinburgh at: http://www.see.ed.ac.uk/ research/IMNS/neural/

Advanced Computer Architecture Group: Neural Networks at the University of York at: http://www.cs.york.ac.uk/arch/NeuralNetworks/

Papers on Neural Networks can be found via Citeseer at: http://citeseer.ist.psu.edu/

Summary

This section has introduced NNs and explained their use within knowledge-based systems.

Self-Assessment Question

Question 1

Fill in the gaps in the following using either ‘neural networks’ or ‘expert systems’ require humans to update their database of information.

use rules in which to make their decisions. adjust to inputs and outputs.

continue to expand their own base of information. emulate human decision making.

learn human thought processes and reasoning patterns.

Question 2

Imagine a NN trained to recognise flowers using 75 sets of data. Another 75 sets of data are used to validate the trained NN. As NNs are trained on a data set the implication is that the bigger the data set the better the training could be. It would therefore be possible for the example problem above to use all 150 data items for training. This would leave none for the validation set. However, the NN could be better trained as it would have more data to learn from. Would this be a good idea?

Answer To Self-Assessment Question

Answer 1

Expert systems require humans to update their database of information. Expert systems use rules and frames in which to make their decisions. Neural networks adjust to inputs and outputs.

Neural networks continue to expand their own base of information. Expert systems emulate human decision making.

Neural networks learn human thought processes and reasoning patterns.

Answer 2

It would not be a good idea to use all of the data for training. Take a simple analogy: imagine a teacher who tells a small child that ‘2 + 2 = 4 and 4 + 4 = 8’. If the child correctly answers the question ‘What does 2 + 2 equal?’ it does not prove that the child has learnt how to add two numbers—they may just be able to recall what the teacher told them. To check if they know how to add we need to give them new, previously unseen problems, such as 3 + 5. In a similar way a NN may learn to remember the inputs it was trained on and the associated outputs. However, we want it to learn how these are connected so that it can solve any problem, i.e., generalise. To test that the NN has learnt to generalise correctly we need to validate the NN using data that was not used in the training process.

Introduction

This section shows how one form of human reasoning can be used within knowledge-based systems.

Objectives

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

explain the use of CBR in the development of knowledge-based systems

evaluate the usefulness of CBR systems.

What Is a Case?

A case has two parts:

Description of a problem or a set of problems

Description of the solution of this problem.

Possible additional components might be explanations, and comments on the quality of the solution, etc.

Cases represent experiences, i.e., they record how a problem was solved in the past.

What Are CBR Systems?

In CBR, information is stored in a KB in the form of cases, rather than rules. When a new situation is encountered, the CBR system reviews the cases to try to find a match for this particular situation. If a match is found, then that case can be used to solve the new problem (see Figure 2.11).

Case-based reasoning works in a similar way to which humans select a course of action from experience.

them to open the printer and check for blockages before any other diagnostic checks are run.

In CBR terms, this might be described as follows:

Technical Diagnosis of Computer Faults

Symptoms are observed (e.g. document fails to print) and values are measured (e.g. level of fluid or toner—half full).

Goal: Find the cause for the failure (e.g. paper jam) and a repair strategy (e.g. open printer and remove blockage).

Case-Based Diagnosis

A case describes a diagnostic situation and contains:

description of the symptoms

description of the failure and the cause

description of a repair strategy.

To perform a diagnosis the following steps must be performed:

store a collection of cases in a case base

find case similar to current problem and reuse repair strategy.

Steps in CBR

A CBR program will normally work through the following steps:

Collect the important features that define each new case presented to the system.

Retrieve past cases matching these features most closely.

Select the cases most relevant to the current problem.

Where necessary, adapt the stored cases to solve the current problem.

Validate the new solution and store as a new case.

Where a case match is not found, then find an alternative solution and record both problem and solution.

Where more that one case match is found, the CBR system will need to:

resolve any ambiguities if multiple solutions are found

recognise that multiple solutions may sometimes be acceptable.

The CBR system is effectively following important processes or rules to:

Retrieve the most relevant cases for comparison with the current problem.

Reuse knowledge from those cases which help in solving the current problem.

Revise the proposed solution, using the case information.

Retain details of the current (now hopefully solved) problem as a new case for future reference.

Cases do not need to be understood by the knowledge engineer in order to be stored.

FIGURE 2.15. Problem comparison.

When Are Two Cases Similar?

Cases are ranked according to their ‘similarity’ and therefore similarity is the most important concept in CBR. We can assess similarity based on the similarity of each feature and similarity of each feature depends on the feature value.

Similarity Computation

The degree of similarity can be expressed by a real number between 0 (not similar) and 1 (identical). These can be assigned to particular features based on experience.

Feature: Problem

However, the importance of different features may be different and there is therefore the need to apply weights to features.

High importance: Problem, Paper empty, Printer ready, . . .

Low importance: Printer type, . . .

Similarity Computation by Weighted Average

Between Problem and Case 1

(see Figure 2.16)

Problem (Symptoms):

1.0Problem (Symptoms)

• Repair: Open printer and remove blockage

Very important feature: weight = 6

Less important feature: weight = 1

FIGURE 2.16. Similarity computation.

Similarity(new, case 1) = 1/25 [6 1.0 + 1 0.5 + 6 1.0 + 6 1.0 + 6 1.0]

= 0.98

Note that the 1/25 component is arrived at from 1/total feature weights, i.e., 1/(6 + 1 + 6 + 6 + 6).

Activity 16

1.Sketch a diagram relating the problem to Case 2.

2.Conduct a similarity computation for Case 2.

3.Determine which of the cases is more similar to the problem.

Feedback 16

Your diagram should look a little like this:

Your calculation should be:

Similarity(new, case 2) = 1/25 [6 1.0 + 1 1.0 + 6 1.0 + 6 0.0 + 6 1.0]

= 0.76

Case 1 is therefore more similar—thus the diagnosis ‘Paper jam’ would be made.