Problem Solving and Search in Artificial
Intelligence

Course Syllabus
Course
Number: 181.190  Lecture with
Exercises (SS 2014) Course
Title: Problem Solving and Search in
Artificial Intelligence Credits: 2,0 Std.
(3ECTSPoints) Lecturer: Priv. Doz. Dr. Nysret Musliu Email: musliu@dbai.tuwien.ac.at 
Course
Description: This course
teaches the main artificial intelligence search techniques used for problem
solving. The emphasis will be in informed search techniques, constraint
satisfaction, advanced heuristic search techniques, and application of
machine learning in search. The course covers these topics: · Basic Concepts · Uninformed Search Strategies · Heuristic Algorithms · Constraint Satisfaction Problems · Constraint Programming Techniques ·
Decomposition Techniques (Tree and Hypertree Decompositions) ·
Metaheuristic Algorithms (Simulated Annealing, Tabu Search, Genetic
Algorithms…) ·
Adversarial Search and Game Playing ·
Application of Machine Learning in Search
(Automated Algorithm Selection, Hyperheuristics) ·
Algorithm Configuration (Automated Parameter Tuning) The use of
search methods will be illustrated by examples, which show the application of
these methods for solving of different hard problems with high practical
relevance. Moreover, successful use of search methods in research and
industrial projects of our group will be presented. Course
Structure: This course
is a combination of lectures and exercises. Students will implement
constraint programming and metaheuristic techniques for a particular problem.
Additionally, during the course different logical problems and puzzles will
be discussed. Students are encouraged to introduce new problems during the
lectures. Literature: Stuart
Russell and Peter Norvig . Artificial Intelligence: A Modern Approach (Third
Edition), 2010 Z.
Michalewicz and D. B. Fogel. How to Solve It: Modern Heuristics, 2nd edition,
SpringerVerlag, 2004 Additionally
the key papers from the literature will be used. … Grading Final
(written) Exam: 60 points Project: 40
points For a positive grade you must obtain at least 50
points and in the final exam you must reach at least 30 points Schedule of
Classes Lectures 06.03.2014 (10:00  11:30) (Seminarraum
Gödel) Preliminary
discussion Basic Concepts (How to Solve It: Modern Heuristics 
Chapters 1, 2 ; Artificial Intelligence: A Modern Approach
(AIMA)  Chapter 3) 13.03.2014 (10:00  12:00) (Seminarraum
Gödel) Uninformed Search Strategies (AIMA  Chapter 3,
How to Solve It  Chapters 3) Informed
Search Algorithms (AIMA  Chapter
4, How to Solve It: Modern Heuristics  Chapters 4) 20.03.2014 (10:00  12:00) (Seminarraum
Gödel) Constraint Satisfaction (AIMA  Chapter 5) 03.04.2014 (10:00  12:00) (Seminarraum
Gödel) Constraint
Programming Techniques (AIMA  Chapter 5) 10.04.2014 (10:00  12:00) (Seminarraum
Gödel) Structural Decomposition Techniques
(Tree/Hypertree Decompositions) 08.05.2014 (10:00  12:00) (Seminarraum
Gödel) Structural Decomposition Techniques
(Tree/Hypertree Decompositions) Metaheuristic
Algorithms: Local Search, Stochastic
Hill Climbing, Simulated Annealing (How
to Solve It  Chapters 3 (sec. 3.2), 4 (sec. 4.1) , 5 (sec. 5.1) 15.05.2014, 23.05.2013 (10:00  12:00) (Seminarraum
Gödel) Metaheuristic
Algorithms: Tabu Search (How to Solve It: Modern Heuristics  Chapters 4,5,6) Evolutionary Algorithms (slides for Chapters 1, 2, 3 in http://www.cs.vu.nl/~gusz/ecbook/ecbookcourse.html
) or (How to Solve It: Modern Heuristics  Chapter
6, Chapter 7(optional)) 22.05.2014 (10:00  12:00) (Seminarraum
Gödel) Application of
Machine Learning in Search (Automated Algorithm Selection) Algorithm Configuration (Automated
Parameter Tuning) Optional:
Adversarial Search and Game Playing (AIMA – Chapter 6) 05.06.2014/12.06.2014 (10:00 – 12:00) (Seminarraum
Gödel) Presentation
of projects 13.06.2014 (16:00 – 18:00) (EI 9 Hlawka HS) Final Exam Project You will implement methods that will be considered in
this class for the Rotating Workforce Scheduling Problem. More information for this problem can be found in
the papers below: N. Musliu, J. Gärtner, W. Slany. Efficient
generation of rotating workforce schedules. Discrete Applied
Mathematics, Vol. 118 (12), pp. 8598, 2002. N. Musliu. Heuristic Methods for Automatic Rotating Workforce
Scheduling. International
Journal of Computational Intelligence Research, Volume 2, Issue 4, pp. 309326, 2006. Benchmark
problems: http://www.dbai.tuwien.ac.at/staff/musliu/benchmarks/workforceScheduling.zip Additional information for this problem will be
given during the class. The project consists of two phases. Phase I:
Implementation of a constraint programming technique for Rotating Workforce
Scheduling Phase II:
Implementation of a metaheuristic technique (or a hybrid technique) for
Rotating Workforce Scheduling 2 students can work together in a group Schedule for the
Assignment: 03.05.2014: Submit in TUWEL your implementation (source code and
the description of your method) for Phase I 03.06.2014 Submit in TUWEL your implementation (source code and
the description of your method) for Phase II 05.06/12.06.2014 (10:00 – 12:00) (Seminarraum
Gödel) Presentation
of projects 
mailto: musliu@dbai.tuwien.ac.at 