IAIP Spring 2007

[Official course description] [Schedule]

News

31.05.07 Error in curriculum: you should read 18.2 and 18.3 not 18.1 and 18.2
29.05.07 Question session moved to Aud. 3
18.05.07 List of students eligible for exam
08.05.07 Official curriculum of IAIP Spring 2007
07.05.07 Question session on Thursday 31.05.07, 13-15 in Aud. 3
07.05.07 Trial exams now available under week 13
20.04.07 Significant changes to exercises for Monday
13.04.07 Slight change to exercises for Monday
26.03.2007 Remember to fill in the course evaluation
26.03.2007 BDD-Based Search moved to lecture 13
23.03.2007 Corrected errors in week 8 exercises
23.03.2007 It is unavoidable that some of you are unable to hand in your projects timely. Requests for extensions are directed to Rune M. Jensen and will be processed on an individual basis.
03.03.2007 Please notice that there are slight changes in the readings for Monday
02.03.2007 Some changes to the exercises for Monday

Time and Place

Lectures: Mondays 13-15 3A12
Recitations: Mondays 15-17 3A14 and computer cluster 3A50 and 3A52
Questions: Thursday 31.05.07 3A12 13-15.
Exam: Monday June 4, 9-13 3A12

Schedule (subject to change)


Lec. Date Teacher Content Slides Readings Exercises
1 29-Jan RMJ Complexity Class P and NP
1. Overview of IAIP
2. The class P and NP
slides RN03 1
G04 1   
(sup. CLRS03 34)
None
2 05-Feb RMJ
[TH]
NP-Completeness
1. Polynomial-time reductions
2. NP-Completeness
3. Proving NP-Completeness
slides G04 2
(skim Domination and Independence proof)
(sup. CLRS03 34)
exercises solutions
3 12-Feb RMJ
[TH]
Uninformed Search
1. Breadth-first search
2. Depth-first search
3. Depth limited search
4. Bidirectional search
slides RN03 3 (except 3.6)
RN03 2 (skim)
exercises solutions
4 19-Feb RMJ
[TH,MA]
Informed Search
1. Best-First Search (BFS)
2. Greedy search, A*
3. Heuristic functions
4. Local search
5. Hill climbing, simulated
    annealing, local beam
    search, genetic algorithms
 

slides

RN03 4 (except page 102-104, 4.4, and  4.5) exercises solutions
5 26-Feb RMJ
[TH,MA]
Game Playing
1. Minimax
2. Alpha-Beta pruning
3. Evaluation functions
4. Expectiminimax
slidesRN03 6 exercises solutions
 
6 05-Mar RMJ
[TH,MA,SA]
Machine Learning
1. Inductive learning
2. Decision tree learning
3. Q-learning
slidesRN03 18.2, 18.3
TM97 13.1, 13.2, 13.3, 13.4
exercises solutions

Game project:
Description
Interface
Java files
Groups
7 12-Mar SS
[TH,MA,SA]
Propositional Logic
1. Syntax and semantics
2. Logical equivalence
3. Resolution
4. Normal forms
5. Horn clauses
6. SAT solving
slides RN03 7 (except 7.7) exercises solutions
 
8 19-Mar RMJ
[TH,MA,SA]
Binary Decision Diagrams
1. Classical representations:
    truth tables, two-level and
    multi-level representations
2. INF normal form
3. Binary Decision Diagrams
    (BDDs)
slides A97 1, 2, 3
exercises solutions

DPLL project
Description
Java files
9 26-Mar RMJ
[TH,MA,SA]
BDD-Based Algorithms
1. Build
2. Apply
3. BDD-based configuration
 
slides
HRA
A97 4.1, 4.2, 4.3, 5, 6, 7
J05
JHRZ06
exercises solutions
 
  02-Apr   Easter break      
  09-Apr   Easter break      
10 16-Apr RMJ
[TH,MA,SA]
Constraint Satisfaction
1. CSPs
2. Constraint Graphs
3. Backtracking
4. Backjumping
5. Backmarking
slides
BBnotes
B05 p.1-13 exercises solutions
BDD project:
Description
Java files
11 23-Apr TH
[TH,MA,SA]

Consistency Techniques
1. Arc consistency
2. i-consistency
3. Directional consistency

slides B05 p.14-19 exercises solutions
12 30-Apr RMJ
[TH,MA,SA]
Advanced Constraint Solving
1. Forward Checking
2. Partial and Full Look-Ahead
3. Variable and Value Ordering
4. Cycle-Cut set
5. Branch and Bound
slides B05 p. 28-42
(except Backtrack-free search p.34-35, and Mace p.36-37)
exercises solutions
Forward checking project:
Description
Java files
13 7-May RMJ
[TH,MA,SA]
Repetition +
1. Quantified Boolean
    Formulas (QBF)
2. BDD-based search
slides JHRZ06 exercises
solutions
trialExam1
trialExam2
IAIPfall05Sol
IAIPfall05NoSol

Exercises and Projects

Each recitation session will have a number of exercises for you to work on. We strongly recommend that you solve each exercise to consolidate what you have learned and prepare for the exam. The exercises, however, are not mandatory. The implementation projects on the other hand are mandatory. They must each be passed in order to qualify for taking the exam. Notice that, as usual for the Danish university educational system, your course grade is solely based on your performance at the exam.

Literature

The textbook (RN) is available in the bookstore at ITU. It costs DDK 603,- (with student discount). Links to PDFs of notes are provided when allowed by copy right laws. It will be informed by email how to get copies from M97.

RN03 Russell, S. and Norvig, P., "Artificial Intelligence. A Modern Approach. Second Edition", Prentice Hall, 2003, ISBN 0-13-080302-2.
G04 Goddard, W., "Introduction to Algorithms - Part 3: P, NP Hard Problems", Lecture note, Clemenson University, 2004
TM97 Mitchell, T.M., "Machine Learning", WCB/McGraw-Hill, ISBN 0-07-042807-7, 1997.
A97 Andersen, H.R., "An Introduction to Binary Decision Diagrams", Lecture note, Technical University of Denmark, 1998.
J05 Jensen, R.M., "Quantified Boolean Formulas", Lecture note, 2005.
JHRZ06 Jensen, R.M, Hansen, E.A., Richards, S., Zhou R., "Memory-Efficient Symbolic Heuristic Search", In Proceedings of ICAPS-06, p. 304-313, 2006.
B05 Barták, R., "Constraint Propagation and Backtracking-Based Search: a brief introduction to mainstream techniques of constraint satisfaction", Lecture note CP Summer School, 2005.

Supplementary Literature

The texts listed below are for students that wish to study a topic in more depth. They are not a part of the course syllabus of IAIP.

CLRS03 Cormen,T.H., Leiserson, C.E., Rivest, R.L. and Stein, C., "Introduction to Algorithms", Second Edition, The MIT Press, 2003, ISBN 0-262-03293-7.

Manning

Rune M. Jensen (RMJ)
Teacher
Office: 3C09
Email: rmj
Tarik Hadzic (TH)
Guest teacher/TA
Office: 3C12
Email: tarik
Sathi Subbarayan (SS)
Guest teacher
Office: 3C12
Email: sathi
Mai Ajspur (MA)
TA
Email: ajspur
Stavros Amanatidis (SA)
TA
Email: samanat