Problem Solving and Search in Artificial Intelligence (WS2020)

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)

Learning Outcomes

• 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.

Prerequisites

Basic knowledge of theoretical computer science and Logic.

In addition, the course and examination will be exclusively in English.

Organisation

The goals can be acquired by studying the lecture material and solving the exercises of the tutorials.

The lectures will be held on Fridays DS1 and the tutorials on Fridays DS2. Please, register here in the OPAL site and also check the schedule for changes.