The aim of this course is to teach main artificial intelligence search techniques used for problem solving in the past years. The emphasis of this course will be in:

• Informed search techniques
• Constraint satisfaction
• Advanced (meta)heuristic search techniques
• Learning and Search

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)

Didactical Concept

• Lectures
• Exercises/project: students will implement an exact or a metaheuristic 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

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 the website of this course: http://www.dbai.tuwien.ac.at/staff/musliu/ProblemSolvingAI/

Assessment

• Written exam(60%)
• Assignments (Project) (40%)

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 papers

(slides: http://www.dbai.tuwien.ac.at/staff/musliu/ProblemSolvingAI/ )