Expert Systems (5 ects)

Current

Increasing your grade? If you are not satisfied with your grade you can increase it by maximum 10 points by solving extra exercises. The deadline is 31.5. 2006
Preliminary course results and evaluation of project works. Notice! I couldn't test all programs, and thus some project works are not fully evaluated, yet. However, the points can only increase from preliminary points. More feedback about your project works on January!
Learning diary points
Check your final exercise points. Please, inform if something is missing (e.g. feedback query point). Exercise points for first exercises
Course evaluation Evaluate the course and get an extra exercise point! (Just inform me that you have filled the query - the answers are anonymous so I cannot check it.)
Schedule for final presentations
Final instructions Comment how to assess project works!
Points for presentations
Teachers: when you evaluate the task solutions, put the points here. Just copy the file and send it to me or print and give the paper copy.
Tell your opinions about assessing learning diaries! The suggested elements can be found here
Have you problems in writing English? Check this!

Description

The idea of expert systems is to simulate human experts in some special problem domain. For example, they can guide the doctor who diagnosizes illness given the patient's symptons, or offer an automatic fault detection system for cars, printers, and other devices.

The course gives a wide introduction to expert systems: how they can be used and how they are constructed, the main approaches to implement them, how to evaluate and compare systems and select the best one for the given problem. The course is implemented as a hybrid of normal lecture-based course and a seminar with emphasized student activity: the students give small teaching sessions to each other and implement an expert system in groups. In addition, each student processes the learnt in her/his individual learning diary.

Prerequicites: Principles of proposition logic and probability theory.

Lectures: Wed 14-16 D106B (14.9.-30.11. 2005)

Registeration: by sending an email to the lecturer Wilhelmiina Hämäläinen (whamalai@cs) before 14.9. Max 20 students.

Approximated workload

Contact sessions: 26 h
Exercises: 26 h
Learning diary: 20 h
Reading literature: >20 h
Projectwork: 40 h
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Total: 132 h = 5 ects

Notice! Excellent grade may require more work!

Preliminary schedule

Date	Topic
14.9.	Introduction to Expert systems, course overview and work allocation
21.9.	Basic types of ESs, rule-based, functional and probabilistic systems
28.9.	Adrian Lemiere and Antti Mikkonen: Fuzzy logic Anahit Poghosova and Fedor Nikitin: Dempster-Shafer theory Konstantin Petrukhnov and Maxim Mozgovoy: Decision trees Further notes on lecture 3
5.10.	Alfiya Ahmetova, Alina Gutnova and Carolina Islas: Truth maintenance systems Jesse Hauninen and Matti Hyvärinen: Bayesian networks (general) Mikko Vinni and Ahmed Hashim: Naive Bayes classifiers Further notes on lecture 4
12.10.	Belinda Ngasia Wafula: Case-based reasoning Lukasz Rakoczy and Tersia Gowases: Genetic algorithms Yuriy Lakhtin and Maxim Dudochkin: Hidden Markov Models Further notes on lecture 5
19.10.	Lukas Obrdlik and Marek Winkler: Neural networks Dominik Wisniewski and Wojciech Wawrzyniak : Support vector machines Beginning projectworks Further notes on lecture 6
26.10.	Comparing and evaluating systems (lecturer) Work specifications (topic + data), short presentations.
2.11.	Work plans (method + how to implement), short presentations. Further notes on lectures 7-8
9.11.	Detailed plans with class diagrams and main algorithms Group works about comparing modelling paradigms Further notes on lecture 9
16.11.	Implementation. 15 min consultation/group
23.11.	Maxim Mozgovoy: Information retrieval for expert systems Testing and documentation should begin.
30.11.	Supersession (4 h), final presentations 20 min per group! Refreshments on break.

Performance

The course performance consists of four parts:

Participating classes (you can drop at most 2/12 classes)
Presentations about main expert systems: A student pair gives a 30 min presentation about some expert system with some lecture material for other students. They should also design some exercise task about the topic and check the solutions. Each exercise task should take approximately 2 hours work.
Exercises: In the beginning of course exercises about introduction lectures and all student presentations. Weekly exercises take approximately 6 hours work in the beginning of course.
Learning diaries: Each student writes a personal learning diary and returns a piece of it weekly. The lecturer will give feedback next week. The learning diaries are evaluated in the end of course. More instructions about learning diaries: http://cs.joensuu.fi/pages/whamalai/tepe/learningdiary.htm
Projectworks: The projectworks are made in groups of three students (other than in the presentations). The documents are written in several phases. In the beginning, the group should return specification document (topic and data), then a design document (how to implement, updated later by class diagrams and main algorithms), small test plan and final implementation documents. All parts should be in the final document.

Evaluation:

Projectworks 40%
Presentations 20%
Learning diaries 20%
Exercises: 20%

Exercises

Exercises 1 (deadline 21.9. 2005)
Exercises 2 (deadline 28.9. 2005)
Material: Modelling (from Hämäläinen: Descriptive and predictive models in educational technology, draft) dataset Linear regression by gnumeric (brief instructions) Notes on exercises 2
Exercises 3.1 (Fuzzy logic), Exercises 3.2 (Decision trees), Exercises 3.3 (Dempster-Shafer theory)
Exercise 4.2 (Bayesian networks) Exercises 4.3 (Naive Bayes classifiers). Exercise 4.1 has been given in paper format. If you are missing, ask the teachers or fetch a paper copy from Wilhelmiina.
Exercise 5.2 (Case-based reasoning) Exercise 5.3 (HMM). Exercise 5.1 (genetic algorithms) is available in paper form in the lecture material? (Deadline 19.10.)
Exercise 6.1 (neural networks). Exercise 6.2 (Support vector machines) (Deadline 26.10.)

Instructions for presentations

Each pair gives a 1/2 h teaching session on the topic. The goal is that the other students really learn! Important points:

How the system works?
Some example for demonstration
How the system is constructed? (from data or by human expert?)
Advantages and disadavantages, restrictions
Applications in expert systems

Hints for searching material:

You can start from the material collected under page Some material for presentations
Try to find the original publication where the system was introduced or publications by the original inventor of the system. They usually describe the system well (and correctly!) and tell plenty of advantages
Check also other sources - the inventors usually don't tell the disadvantages or restrictions
There are several tutorials in the net - they are easy-reading, but there is no guarantees that they are correct!

In addition, remember to design one bigger or two smaller tasks about your lecture. The task should require about 2 hours work. Give also some lecture material so that the other students can solve the tasks. (You can ask the teacher to take copies before the session.)

Topics for presentations

Truth-maintenance systems - logical rule-based systems
Default rules - describing exceptions to logical rules
Fuzzy logic - logical rules with fuzzy truth-values
Dempster-Shafer theory - an alternative to probabilistic rules
Decisions trees - deterministic classification
Naive Bayes classifiers - probabilistic calssification
Neural networks - functional systems for prediction and classification
Support vector machines - a competitor for neural networks?
Instance-based learning: k-nearest neighbours and case-based reasoning - reasoning from similar cases
(Hidden) Markov models - describing dynamic processes
Genetic algorithms - simulating evolution process

Project works

Literature

Mitchell, T.M.: Machine Learning. McGraw-Hill, 1997. Old classic, very comprehensive!
Russell, S.J. and Norvig, P.: Artificial Intelligence: A modern approach. Prentice-Hall, 2nd edition 2002. Another classic, which covers several systems. Especially parts V (Uncertain knowledge and reasoning) and VI (Learning), and chapters 2, 7, and 10.
Luger, G.F.: Artificial Intelligence. Structures and strategies for complex problem solving. Addison-Wesley, 2002. (chapters 7, 9, 10, 11)
Paris, J.B.: The uncertain reasoner's companion. A mathematical perspective. Cambridge University Press, 1994. (Expressing uncertainty by probabilities and other measures, calcyla for systems, some comprison. Mathematical, as mentioned.)
Kantarzic: Data mining: concepts, models, methods, and algorithms. Wiley-IEEE Press, 2002. (Describes also some methods for ESs)
Walley, P.: Measures of uncertainty in expert systems. Artificial Intelligence, 83, 1996. (Comparison between Bayes, Dempster-Shafer and ideal. Good criteria for evaluation.)
Hämäläinen, W.: Search for an ideal reasoning system: specification of the minimal requirements. http://cs.joensuu.fi/~whamalai/articles/graduarticle.pdf (Compares Bayesian networks, Dempster-Shafer theory and TM's. Gives general evaluation criteria for reasoning systems.)
Some material for presentations

Data for project works

Data is essential for project works, and you try to invent a good topic by checking available data. Below you can find links to some data repositories. Notice that some data sets are too small for our purposes. Select a data set, which contains at least 50 rows of data! You can also search/construct data sets of your own! For example, you can interview other students or collect data by internet queries. In addition you can use expert knowledge (interview some experts, search literature, and statistics).

Machine Learning Repository A lot of datasets.
Several interesting datasets!
UNESCO databases
Time series data
Economical data
Government data?