KIIS: Artificial Intelligence and Intelligent Systems 2008

A Computer Science course given in the fall semester of 2008 at Roskilde University by Henning Christiansen.

General information

Unless otherwise stated, the course runs on Tuesdays, 9.30-12.00 and 13.00-15.30, in Room 43-2.43

Course material, notes, references, software, manuals, links to external sources such as manuals, etc. are collected in this page and linked below when relevant.

You will need the course book [MN1] and the note [HC1] from day 1 (or even before for your preparation).
LOOK HERE FOR MORE INFORMATION.

How to plan your work

Each course day is planned an integrated whole covering a specific topic, and may include lectures, practical or theoretical exercises, discussions and possibly student presentation.

The reading for each day will be indicated in the schedule below. It is strongly recommended that you look at the text both before the course day, so that you can get the most out of that day, and after the course day, so that you are sure you understand everything.
We will work together on some or all of the exercises given in the schedule for the course day. It is suggested that you do not work on the exercises in advance, so that all student can work in the same pace when we are together. Unless otherwise stated, you are expected to continue with any remaining exercises as home work.
Depending on how difficult they turn out to be, we may or may not go through a solution the following course day.
It is practical if you can bring your own laptop so you can install the software that we use, and so you easily can continue at home. If you do not have a laptop available, we will have a computer ready for you.
If you are ill or in other ways miss out a course day, it is important that you find time to go through all material including the exercises by yourself until you understand everything fully. This especially as the next week, when you are back, we work on the next new topic. (And it is not so fun, if your exam question happens to concern the 1/10 of the course that you do not know anything about).

Course schedule

The schedule below will be maintained as the course goes along; when the course is finished, the "Reading" column (minus "Background reading") defines the exam curriculum.

Date Theme Reading Exercises, guidelines, etc.

1 2-sep-2008 Introduction to AI and the course; first Prolog workshop
Slides1, slides2 (large and strange files) [MN] chap 1; [HC1]chapter 1 and parts of 2.
Background reading: [AT] [HC1], exercise 2.1 and 2.2
Source texts: exercise2_1, circuits.

2 9-sep-2008 Prolog workshop continued; Rule-based expert systems; More workshop: Constraint Handling Rules in Prolog
Slides1, Slides2, Slides3. [MN] chap 2 minus 2.7; [HC1] chap's 2 minus 2.4, chap's 3, 5 and 7.
Background reading: [WRZA]; [MN] section 2.7 (nb: example difficult to understand) Exercises: [HC1] 5.1, 5.2 (only part), 5.3 + extra presented at lecture, 7.1 and perhaps 7.2 (perhaps more)
Source texts: circuits. vertihori.txt, my_kb0Prolog.txt, expert0.txt, my_kb0.txt, gcd.txt, primes.txt, fibo.txt, kb0_as_CHR.txt.
Alternative versions (tested in SICStus 4 and should work in SWI): expert04.txt, gcd4.txt, primes4.txt, fibo4.txt, kb0_as_CHR4.txt.

3 16-sep-2008 Deduction, Induction, and Abduction - with special emphasis on abduction. Application of abduction to diagnosis problem.
Slides. [HC1] chapters 7, 8 minus 8.9.
Background reading: (alternative description of the above) [HC2] pages 1-17 (middle); [HC1] the exercise of section 9.2, question 1.
Exercise about CHR.
Source texts: db.txt, diagnosisPeriodic.txt, diagnosisConsistent.txt, deductivePower.txt, toolsCHRexercise.txt.

4 23-sep-2008 Statistics and Bayesian reasoning, Bayesian networks
Slides [MN] chapter 3, until p. 62 middle (check comments in the note below:)
Examples and exercises for conditional probabilities and Bayesian reasoning
Charniak: Bayesian Networks without Tears. AI Magazine 12(4): 50-63 (1991); skip from p. 53 "In the rest of this section, I define..." until the section headed "Consistent probabilities" p. 55. Skip also from p. 56 "Evaluation networks" and the rest of the paper.
Notice the error in fig. 2: a negation sign missing; it should read P(do | ¬ fo ¬ bd) = .3 Exercises of Examples and exercises for conditional probabilities and Bayesian reasoning

5 30-sep-2008 More on probabilistic methods: The PRISM system, implementing Bayesian networks.
Slides as pdf Course notes: NEW VERSION 29-sep-2008: Logical-Statistical models and parameter learning in the PRISM system, minus section 3 (optional for those with an interest in biological sequence data).
Complementary reading (optional): Hidden Markov Models and their implementation in the PRISM system Exercise 5.2 of Logical-Statistical models and parameter learning in the PRISM system.
Source texts: famOut.psm, famOutData.dat, hmm1.psm, hmm2.psm, hmm3.psm, words.dat.

6 7-oct-2008 Natural Language Analysis with Definite Clause Grammars; discourse analysis with CHR.
Starting the written assignments
[BBS]. (pdf document) chapters 7 and 8, minus 7.2.3 and 8.1.3; Natural language analysis with DCG and CHR: Examples and exercises
See note on the written assignments.
All exercises of the course note.
NB: More exercises to be added
Source texts: dcg1, dcg2, trip, discourse1, discourse2.

7 14-oct-2008 More on Natural Language Analysis: Assumptions in Hyprolog Natural language analysis with assumptions in Hyprolog: Introduction, examples and exercises.
User's guide to the HYPROLOG system: A logic programming language with assumptions and abduction
We will use the Hyprolog system which you can download here.
NB: The version avaiable 9-oct-2008 works only in SICStus Prolog 3, but versions for SICStus Prolog 4 and SWI Prolog will be avaiable in a new version which will be uploaded before 14-oct-2008.
Exercises of course note Natural language analysis with assumptions in Hyprolog: Introduction, examples and exercises.
Notice that the note also contains a proposal for a written assignment by extending one of the natural language examples.
Source text: simpleAssump,
solutionEx2 (solution to an exercise).

8 21-oct-2008 Written assignment No normal course session but your teacher will be available (at his office) all day for helping you with the assigments.

9 28-oct-2008 Artificial neural networks
Powerpoint slides by Angshuman Saha. [MN] chap. 6, until and including 6.5; the rest of chap. 6 should be viewed as background material. NB: You are not expected to be able to reproduce the details of the book's formulas for adjusting weights, but you should understand the overall feed-forward, back-propagation mechanism.
We will also look at one or both of two tools for building and training neural nets which you can find at http://www.geocities.com/adotsaha/NNinExcel.html, produced by Angshuman Saha; you should read this page and the explanations provided by the two tools. NB: These tools run as Excel sheets, so they should be easy to run open and test. Exercises: Note with exercises about Neural Networks, question 7.1-7.4; if time 7.5. Questions 7.5-7.6 recommended as homework.

10 4-nov-2008 Fuzzy expert systems
Student presentation of the status for their written assignments [MN] chap 4; see important notes here.
Backgrund reading: [BNW] gives an alternative presentation of fuzzy logic and control. Exercises about Fuzzy Control: Driving car using fuzzy logic; exercise 1-3, perhaps 4 and 5 if time permits.

12-nov-2008 LAST DAY FOR GIVING IN THE WRITTEN ASSIGNMENT Send by email to your teacher

11 11-nov-2008 Evolutionary computing
(We use parts the textbook's slides, lectures 9+10 [MN] chap. 7 Skip section 7.4, and be aware that there are several problematic and unclear points in this chapter; see comments.
Background reading: The following article lists interesting applications of EC/GP. J.R. Koza, M.A. Keane, M. J. Streeter: Evolving Intentions. Scientific American, Feb 2003, Vol. 288, Issue 2 (pp. unknown). Available online when you search via http://www.rub.ruc.dk (works only from RUC or VPN to RUC).
You may also search for references concerning antenna design, where EC/GP has had success. Exercises on genetic programming.

QA ??-jan-2009
10.00-14.00 (?) Question-and-answer session
Preparation for the exam Please send questions or topics you want explained in advance to your teacher. If no questions or indications of interest have been received by a deadline to be specified, this session is cancelled.

X 5-jan-2009 EXAM About the exam
Sometime during the course (probably near the end), each student will prepare a written assigment, which is handed in and given to the examiner/external examiner. There will be a slot of total 30 min's for the exam of each student, including examiner's evaluation. Examination may take about 15-20 min's, and you are expected to start with a presentation of your written assignment of perhaps 5 to 7 min's, which will be followed by a discussion. The examiners will most likely also ask a question to part of the course literature which is not directly related to your assignment but can be anywhere in the course curriculum. An advice: The presentation of the assignment needs not be fancy, it's the content that matters. A possible structure could be: Which problem did I approach, how did I approach it, what did work and what did not, and what did I learn ....
Another advice: The report that you gave in for the assignment is not in itself assessed in the exam, so if you you are aware of any weak points, you need not "defend" or "repair" but you can (if you like) take these as points for discussion. If there is any doubt, contact your teacher.

	Date	Theme	Reading	Exercises, guidelines, etc.
1	2-sep-2008	Introduction to AI and the course; first Prolog workshop Slides1, slides2 (large and strange files)	[MN] chap 1; [HC1]chapter 1 and parts of 2. Background reading: [AT]	[HC1], exercise 2.1 and 2.2 Source texts: exercise2_1, circuits.
2	9-sep-2008	Prolog workshop continued; Rule-based expert systems; More workshop: Constraint Handling Rules in Prolog Slides1, Slides2, Slides3.	[MN] chap 2 minus 2.7; [HC1] chap's 2 minus 2.4, chap's 3, 5 and 7. Background reading: [WRZA]; [MN] section 2.7 (nb: example difficult to understand)	Exercises: [HC1] 5.1, 5.2 (only part), 5.3 + extra presented at lecture, 7.1 and perhaps 7.2 (perhaps more) Source texts: circuits. vertihori.txt, my_kb0Prolog.txt, expert0.txt, my_kb0.txt, gcd.txt, primes.txt, fibo.txt, kb0_as_CHR.txt. Alternative versions (tested in SICStus 4 and should work in SWI): expert04.txt, gcd4.txt, primes4.txt, fibo4.txt, kb0_as_CHR4.txt.
3	16-sep-2008	Deduction, Induction, and Abduction - with special emphasis on abduction. Application of abduction to diagnosis problem. Slides.	[HC1] chapters 7, 8 minus 8.9. Background reading: (alternative description of the above) [HC2] pages 1-17 (middle);	[HC1] the exercise of section 9.2, question 1. Exercise about CHR. Source texts: db.txt, diagnosisPeriodic.txt, diagnosisConsistent.txt, deductivePower.txt, toolsCHRexercise.txt.
4	23-sep-2008	Statistics and Bayesian reasoning, Bayesian networks Slides	[MN] chapter 3, until p. 62 middle (check comments in the note below:) Examples and exercises for conditional probabilities and Bayesian reasoning Charniak: Bayesian Networks without Tears. AI Magazine 12(4): 50-63 (1991); skip from p. 53 "In the rest of this section, I define..." until the section headed "Consistent probabilities" p. 55. Skip also from p. 56 "Evaluation networks" and the rest of the paper. Notice the error in fig. 2: a negation sign missing; it should read P(do \| ¬ fo ¬ bd) = .3	Exercises of Examples and exercises for conditional probabilities and Bayesian reasoning
5	30-sep-2008	More on probabilistic methods: The PRISM system, implementing Bayesian networks. Slides as pdf	Course notes: NEW VERSION 29-sep-2008: Logical-Statistical models and parameter learning in the PRISM system, minus section 3 (optional for those with an interest in biological sequence data). Complementary reading (optional): Hidden Markov Models and their implementation in the PRISM system	Exercise 5.2 of Logical-Statistical models and parameter learning in the PRISM system. Source texts: famOut.psm, famOutData.dat, hmm1.psm, hmm2.psm, hmm3.psm, words.dat.
6	7-oct-2008	Natural Language Analysis with Definite Clause Grammars; discourse analysis with CHR. Starting the written assignments	[BBS]. (pdf document) chapters 7 and 8, minus 7.2.3 and 8.1.3; Natural language analysis with DCG and CHR: Examples and exercises See note on the written assignments.	All exercises of the course note. NB: More exercises to be added Source texts: dcg1, dcg2, trip, discourse1, discourse2.
7	14-oct-2008	More on Natural Language Analysis: Assumptions in Hyprolog	Natural language analysis with assumptions in Hyprolog: Introduction, examples and exercises. User's guide to the HYPROLOG system: A logic programming language with assumptions and abduction We will use the Hyprolog system which you can download here. NB: The version avaiable 9-oct-2008 works only in SICStus Prolog 3, but versions for SICStus Prolog 4 and SWI Prolog will be avaiable in a new version which will be uploaded before 14-oct-2008.	Exercises of course note Natural language analysis with assumptions in Hyprolog: Introduction, examples and exercises. Notice that the note also contains a proposal for a written assignment by extending one of the natural language examples. Source text: simpleAssump, solutionEx2 (solution to an exercise).
8	21-oct-2008	Written assignment	No normal course session but your teacher will be available (at his office) all day for helping you with the assigments.
9	28-oct-2008	Artificial neural networks Powerpoint slides by Angshuman Saha.	[MN] chap. 6, until and including 6.5; the rest of chap. 6 should be viewed as background material. NB: You are not expected to be able to reproduce the details of the book's formulas for adjusting weights, but you should understand the overall feed-forward, back-propagation mechanism. We will also look at one or both of two tools for building and training neural nets which you can find at http://www.geocities.com/adotsaha/NNinExcel.html, produced by Angshuman Saha; you should read this page and the explanations provided by the two tools. NB: These tools run as Excel sheets, so they should be easy to run open and test.	Exercises: Note with exercises about Neural Networks, question 7.1-7.4; if time 7.5. Questions 7.5-7.6 recommended as homework.
10	4-nov-2008	Fuzzy expert systems Student presentation of the status for their written assignments	[MN] chap 4; see important notes here. Backgrund reading: [BNW] gives an alternative presentation of fuzzy logic and control.	Exercises about Fuzzy Control: Driving car using fuzzy logic; exercise 1-3, perhaps 4 and 5 if time permits.
	12-nov-2008	LAST DAY FOR GIVING IN THE WRITTEN ASSIGNMENT	Send by email to your teacher
11	11-nov-2008	Evolutionary computing (We use parts the textbook's slides, lectures 9+10	[MN] chap. 7 Skip section 7.4, and be aware that there are several problematic and unclear points in this chapter; see comments. Background reading: The following article lists interesting applications of EC/GP. J.R. Koza, M.A. Keane, M. J. Streeter: Evolving Intentions. Scientific American, Feb 2003, Vol. 288, Issue 2 (pp. unknown). Available online when you search via http://www.rub.ruc.dk (works only from RUC or VPN to RUC). You may also search for references concerning antenna design, where EC/GP has had success.	Exercises on genetic programming.
QA	??-jan-2009 10.00-14.00 (?)	Question-and-answer session Preparation for the exam	Please send questions or topics you want explained in advance to your teacher. If no questions or indications of interest have been received by a deadline to be specified, this session is cancelled.
X	5-jan-2009	EXAM	About the exam Sometime during the course (probably near the end), each student will prepare a written assigment, which is handed in and given to the examiner/external examiner. There will be a slot of total 30 min's for the exam of each student, including examiner's evaluation. Examination may take about 15-20 min's, and you are expected to start with a presentation of your written assignment of perhaps 5 to 7 min's, which will be followed by a discussion. The examiners will most likely also ask a question to part of the course literature which is not directly related to your assignment but can be anywhere in the course curriculum. An advice: The presentation of the assignment needs not be fancy, it's the content that matters. A possible structure could be: Which problem did I approach, how did I approach it, what did work and what did not, and what did I learn .... Another advice: The report that you gave in for the assignment is not in itself assessed in the exam, so if you you are aware of any weak points, you need not "defend" or "repair" but you can (if you like) take these as points for discussion. If there is any doubt, contact your teacher.

Last modification 3-jan-2009, Henning Christiansen