Problem solving and search is a central topic in Artificial Intelligence. This course presents several techniques to solve in general difficult problems.
The course covers the following topics:
- Basic Concepts
- Uninformed vs Informed Search
- Local Search, Stochastic Hill Climbing, Simulated Annealing
- Tabu Search
- Answer Set Programming
- Constraint Satisfaction
- Evolutionary Algorithms, Genetic Algorithms
- Structural Decomposition Techniques (Tree/Hypertree Decompositions)
- The students should identify why typical AI problems are difficult to solve
- The students will analyze different algorithms and methods for AI problems and identify when their application is appropriate
- The connections between the (graph) structure and the complexity of a problem should become clear, as well as which methods can be used to tackle the problem
- In the practical part, the students will analyze a given problem and develop a solution for it.
Basic knowledge of theoretical computer science and Logic.
The goals can be acquierd by studying the lecture material, solving the exercises of the tutorials and developing an implementation for a practical problem.
The practical work should be performed in groups of two students throughout the semester with regular updates on the progress.
The lecture will be on Tuesday DS2 and the tutorials on Thursday DS1. Please check the concrete schedule for changes.
- Created on
- 04/04/2020 at 10:43 AM
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- 19/04/2021 at 09:47 AM