181.190 Problem Solving and Search in Artificial Intelligence
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2023S, VU, 2.0h, 3.0EC
TUWELQuinn ECTS survey


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Presence

Learning outcomes

After successful completion of the course, students are able to:

  • Develop and apply uninformed and informed search methods
  • Design and implement (meta)heuristic methods for various problems
  • Model problems with constraint programming (CP) modeling languages and SAT
  • Use CP/SAT for solving various problems
  • Understand concepts regarding tree/hypertree decompositions and be able to use (hyper)tree decompositions in problem solving
  • Explain methods that are used for automated algorithm configuration and algorithm selection
  • Apply automated algorithm selection and configuration for various problems/algorithms

Subject of course


  • 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)


Teaching methods

  • Lectures
  • Exercises/project: students will implement an exact and a metaheuristic/hybrid method for a particular problem
  • Discussion for solving of different logical problems and puzzles
  • Presentation of solution methods from students
  • Demonstration of applications developed in research and industrial projects of our group

Mode of examination


Additional information

The preliminary discussion/first lecture will take place on 14.03.2023 (16:15 - 17:45) 



ECTS Breakdown:

9 classes (including preparation): 25 h

project (including presentation): 25 h

exam: 25 h


total: 75 h


For latest information, please visit TUWEL



Course dates

Tue16:00 - 18:0014.03.2023 - 27.06.2023FAV Hörsaal 1 - INF Lectures
Fri13:00 - 16:0030.06.2023FAV Hörsaal 1 - INF Assignment 2
Problem Solving and Search in Artificial Intelligence - Single appointments
Tue14.03.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue21.03.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue28.03.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue18.04.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue25.04.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue02.05.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue09.05.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue16.05.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue23.05.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue06.06.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue13.06.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue20.06.202316:00 - 18:00FAV Hörsaal 1 - INF Lectures
Tue27.06.202316:00 - 18:00FAV Hörsaal 1 - INF Exam
Fri30.06.202313:00 - 16:00FAV Hörsaal 1 - INF Assignment 2

Examination modalities


  • Written exam(50%)
  • Assignments (Project) (50%)

Open book exam.

Course registration

Begin End Deregistration end
18.01.2023 10:00 19.03.2023 15:00 20.03.2023 18:00

Registration modalities




Z. Michalewicz and D. B. Fogel. How to Solve It: Modern Heuristics, 2nd edition, Springer-Verlag, 2004

Artificial Intelligence: A Modern Approach (Third Edition) by Stuart Russell and Peter Norvig; Prentice Hall, 2010.

Different scientific paper

Slides: TUWEL


Previous knowledge

Knowledge of algorithms and data structures

Programming skills



Accompanying courses