Lectures
Basic topics focused on symbolic artificial intelligence. These topics covers requirements for the state exam. These topics will be part of the exam in the summer semester of the academic year 2019/2020 provided the exam will take place.
| Topic | Slides | |
|---|---|---|
| 1,2 | Definitions of artificial intelligence, history, Turing test. Non-informed state-space search | PDF, PDF, PDF |
| 3 | Informed state-space search: Dijkstra, heuristics, Greedy search, A* | PDF, PDF |
| 4 | Optimization, local search algorithms, QUBO | PDF, PDF, PDF |
| 5 | Automated planning, classical planning, actions | |
| 6 | Robotics, robotic hardware, motion planning | |
| 7 | Agents, simple decision making, game theory, game tree search | PDF, PDF, PDF |
| 8 | Problem modeling, CSP, SAT, direct encoding | |
| 9 | Decision making in real non-deterministic environments, MDP, POMDP | |
| 10 | Evolutionary computation, genetic algorithm |
Extra material will be included in lectures according to the time constraints.
References
[1] S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach (Third Edition). ISBN: 978-0136042594. Prentice Hall, 2009.