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

Activity 17

The table below shows some characteristics of both RBR and CBR. The details about RBR have been entered into the table. Complete the table for CBR, remembering that CBR examples are likely to be the opposite of RBR.

Feedback 17

Information on CBR is given in the third column.

Current Research in CBR

The following web addresses contain information on research in CBR:

Artificial Intelligence Applications Institute: University of Edinburgh (makers of the AIAI CBR tool) at: http://www.aiai.ed.ac.uk/technology/casebasedreasoning.html

Norwegian University of Science and Technology: Al and Learning Group at: http://www.idi.ntnu.no/grupper/ai/cbr/

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

Summary

In this section you have been introduced to CBR and learned that information in CBR systems is stored in a KB in the form of cases, rather than rules.

You also learned that the use of CBR systems is appropriate where problems cannot be easily decomposed or where the general principles involved are not completely understood, but where a library of past experience can be generated.

Self-Assessment Questions

Question 1

Describe how CBR can be used in searching the Internet for information.

Question 2

The CBR system shown earlier is used to diagnose printer faults. Imagine a new fault occurs which the CBR system incorrectly diagnoses. Explain what happens next.

Later the fault is correctly diagnoses as ‘No power’ and a solution is provided ‘Check electrical supply’. One of the printers symptoms was ‘Status lights: Off’. Making up the other symptoms and using the format you saw earlier in the chapter construct a diagram to represent the newly solved problem.

Answers to Self-Assessment Questions

Answer 1

Go to the AskJeeves website and ask the search engine a question. Rather than provide a list of sites straight away that may answer your question, Jeeves sometimes asks additional questions to clarify your initial question.

Activity 19

This activity will give you the opportunity to visualise genetic algorithms.

Visit the site http://www.rennard.org/alife/english/gavintrgb.html.

Follow the link to the Genetic Algorithm Viewer.

Detailed instructions and explanations are available on the site.

Note that the Java applet and source code can be downloaded from the front page.

When a genetic algorithm is implemented it is usually done in a manner that involves the following components:

a genetic representation for potential solutions to the problem

a way to create an initial population of potential solutions

an evaluation function that plays the role of the environment, rating solutions in terms of their ‘fitness’

genetic operators that alter the composition of children

values for various parameters that the genetic algorithm uses.

The actual process is cyclical and repeats until an optimum solution is obtained:

1.Evaluate the fitness of all of the individuals in the population.

2.Create a new population by performing operations such as crossover, fitnessproportionate reproduction and mutation on the individuals whose fitness has just been measured.

3.Discard the old population and iterate using the new population.

One iteration of this loop is referred to as a generation. The first generation (generation 0) of this process operates on a population of randomly generated individuals. From there on, the genetic operations, in concert with the fitness measure, operate to improve the population.

Example of Genetic Algorithms

Let’s say we have a pool of random letters and ‘space’ characters, strung into series of length 37. We are attempting to assemble the following sentence out of this primordial soup of letters and spaces:

jackdaws love my big sphinx of quartz

All we know about this sentence is that it is 37 characters in length, is composed up of the letters a–z and spaces, and that we have a function called fitness(string),

which accepts our test strings and returns a fitness value, i.e., tells us how many of the characters are correct.

First we make a set of 37 character random strings of letters. For this example, we’ll just use a set of four strings. In practice, hundreds or thousands would be applied to the task.

Here are the four random strings, matched up against the actual sequence, as well as their fitness values:

When each member of the population is evaluated we can see that strings 1 and 3 have the best fitness score. Strings 2 and 4 can therefore be discarded.

Incidentally, the average fitness for this first population is (10 + 3 + 8 + 3)/ 4 = 6

We then crossover portions of the genetic material from each of the remaining members of the population to generate the next generation. We can generate a new string combining 1 and 3 by taking the first letter from String 1 followed by three letters from String 3 then another from 1 and three from 3 and so on.

Repeating the process but this time taking the first letter from String 3 and the next three letters from String 1 will give us two new strings.

jawe resa fi p cmdebs hsnon o fila mp

nzagguwn apdoexmdvni asromnaio e xar-

(The ‘ ’ character is used here to indicate the presence of a space at the end of the string.)

generating one possible examination timetable would be a typical application of genetic algorithms. If you imagine several thousand students all sitting examinations over a 2-week period there would be many billions of possible examination timetables. However, the vast majority of these would involve one or more clashes, where a student would need to take two or more examinations at the same time. Clearly this is impossible. The goal would be too find an examination timetable with no clashes. Further no student would like to sit five examinations in 5 days (nor two in the same day) therefore an ideal timetable would be one where all examinations are evenly spread out. Given the immense difficulty in finding this one ideal timetable (assuming it exits), from among the many billions of possible timetables, we may be willing to accept any of the very good timetable solutions available, i.e., any timetable where there are no clashes and no student has two examinations in 1 day (though they may have three or four in 1 week). This is where genetic algorithms excel. They are very good at finding one of the many hundred of good solutions to such a problem.

Complex problems can be solved using other techniques, but it is in the rapid development of set of good solutions where genetic algorithms are particularly useful.

Other Examples of Genetic Algorithms

You will find examples of actual genetic algorithms on the Internet at:

http://ai.bpa.arizona.edu/ mramsey/ga.html

http://ai.bpa.arizona.edu/ tong/gaoi/

You will even find a genetic algorithm applied to playing jazz solos at:

http://www.it.rit.edu/ jab/GenJam.html

The genetic algorithm playground (a general purpose genetic algorithm toolkit where you can define and run your own optimisation problems) is available for download at:

http://www.aridolan.com/ofiles/ga/gaa/gaa.aspx#Download

Instructions are included.

Processes Within Genetic Algorithms

As already noted, genetic algorithms try to mimic evolution. To do this, they use three basic processes.

Reproduction

Production of new generations of code using parents with higher fitness ratings, that is having a higher probability of finding the answer to problems.

Crossover

Changing the code within two strings at a random place and creating two new strings of code by merging the ‘split’ strings. This process is used in nature where genes from parents combine to form a whole new chromosome.

Mutation

Changing one digit in the code on a random basis. For example, changing a 1 to a 0 without the processes of reproduction or crossover. This mimics random changes in genetic code and is especially useful where crossover does not provide an answer.

Use of Genetic Algorithms

Genetic algorithms are used to solve many large problems including:

scheduling

transportation

layout and circuit design.

Two very detailed applications of genetic algorithms are available at:

A Genetic Algorithm Based University Timetabling System http://www.asap.cs.nott.ac.uk/publications/pdf/crimea94.pdf

General Aviation Aircraft (GAA): Analysis of a Product Family Platform using Genetic Algorithm

http://www.personal.psu.edu/users/m/v/mvd119/doc/gapaper 1 .doc

Current Research in Genetic Algorithms

The following web addresses contain information on research in genetic algorithms at:

AIAI, the Artificial Intelligence Applications Institute at the University of Edinburgh

http://www.aaai.org/AITopics/html/genalg.html

Indian Institute of Technology Kanpur Genetic Algorithms Laboratory http://www.iitk.ac.in/kangal/

Heuristics and Artificial Intelligence in Finance and Investment http://www.geocities.com/francorbusetti/

Papers on Genetic Algorithms can be found via Citeseer http://citeseer.ist.psu.edu/

Summary

This section has provided an introduction to genetic algorithms and shown how the process of applying them mimics the biological evolutionary process.