Problem Solving and Search in Artificial
Intelligence

Course Syllabus
Course Number: 181.190  Lecture with Exercises (SS 2017) 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 Lectures 09.03.2017 (10:00 
12:00) (Seminarraum Gödel) Preliminary discussion Basic Concepts (How to Solve It: Modern Heuristics  Chapters 1, 2 ; Artificial Intelligence: A Modern Approach (AIMA)  Chapter 3) 16.03.2017 (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) 06.04.2017 (10:00  12:00) (Seminarraum Gödel) Constraint Satisfaction (AIMA  Chapter 5) 27.04.2017 (10:00  12:00) (Seminarraum Gödel) SAT encodings Discussion in the
class for problems: NQueens, Sudoku, Graph Coloring, Rotating Workforce Scheduling 04.05.2017 (10:00  12:00) (Seminarraum Gödel) Structural Decomposition Techniques (Tree/Hypertree Decompositions) 11.05.2017 (10:00 
12:00) (Seminarraum Gödel) 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) 18.05.2017 (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)) 01.06.2017 (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) 22.06.2017 (09:00 –
12:00) (Seminarraum Gödel) Presentation of projects 23.06.2017
(16:00 – 18:00) (EI
9 Hlawka HS) Final Exam (Two other exams will take place in
October/November) Project You will implement
methods that will be considered in this class for the VeRoLog solver challenge. More information for
the assignment will be given in lectures. The project consists
of two phases. Phase I: Implementation of a complete AI (constraint programming, SAT, …)
technique for this problem Phase II: Implementation of a metaheuristic technique (or a hybrid technique)
for this problem You can use
different (meta)heuristic methods and constraint modeling languages/solvers
such as MiniZinc (see http://www.minizinc.org/ ), the solver
that was developed by Markus Triska at
the TU Wien (see http://eu.swiprolog.org/man/clpfd.html),
… 2 students can work
together in a group Schedule for the Assignments: 15.06.2017 Submit in TUWEL your
implementation (source code and the description of your method) for Phase I
or II 22.06.2017 (09:00 –
12:00) (Seminarraum Gödel) Presentation of projects 
mailto: musliu@dbai.tuwien.ac.at 