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

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)

• 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

The preliminary discussion/first lecture will take place on 05.03.2024 (15:15 - 16:45)

ECTS Breakdown:

9 classes (including preparation): 25 h

project (including presentation): 25 h

exam: 25 h

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total: 75 h

For latest information, please visit TUWEL

Assessment

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

Open book exam.

Ort: TISS

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

Knowledge of algorithms and data structures

Programming skills