Problem Solving and Search in Artificial Intelligence
From International Center for Computational Logic
Problem Solving and Search in Artificial Intelligence
Course with SWS 2/2/2 (lecture/exercise/practical) in SS 2020
Lecturer
Tutor
SWS
- 2/2/2
Modules
Examination method
- Oral exam
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.
Organisation
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.
Announcement: because of the COVID-19 pandemic we will provide the materials for self-learning on the planed dates for lectures and tutorials. Please register for the course in OPAL:
https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/23291363328- Stuart J. Russell and Peter Norvig. "Artificial Intelligence A Modern Approach" (3. edition ). Pearson Education, 2010.
- Zbigniew Michalewicz and David B. Fogel. "How to Solve It: Modern Heuristics", volume 2. Springer, 2004.
- Martin Gebser, Benjamin Kaufmann Roland Kaminski, and Torsten Schaub. "Answer Set Solving in Practice". Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers, 2012.
- Michael Gelfond and Vladimir Lifschitz. "Classical negation in logic programs and disjunctive databases". New Generation Comput., 9(3–4):365–386, 1991.
- A.E. Eiben and J.E. Smith. "Introduction to Evolutionary Computing", Springer, 2003.
- Thomas Hammerl, Nysret Musliu and Werner Schafhauser. "Metaheuristic Algorithms and Tree Decomposition", Handbook of Computational Intelligence, pp 1255–1270, Springer, 2015.
- Hans L. Bodlaender, Arie M.C.A. Koster. "Treewidth computations I. Upper bounds", Comput. 208(2): 259–275, 2010.
- Georg Gottlob, Nicola Leone, and Francesco Scarcello. "Hypertree decompositions and tractable queries", Journal of Computer and System Sciences, 64(3):579–627, 2002. ISSN 0022-0000.
- Artan Dermaku, Tobias Ganzow, Georg Gottlob, Ben McMahan, Nysret Musliu, and Marko Samer. "Heuristic methods for hypertree decomposition", In Alexander Gelbukh and Eduardo F. Morales, editors, MICAI 2008: Advances in Artificial Intelligence, volume 5317 of LNCS, pages 1–11. Springer Berlin Heidelberg, 2008. ISBN 978-3-540-88635-8.
Subscribe to events of this course (icalendar)
| Lecture | Introduction | DS2, April 14, 2020 in APB E005 | File 1, File 2, File 3 |
| Lecture | Informed vs. Uninformed Search | DS1, April 16, 2020 in APB E005 | File 1, File 2 |
| Lecture | Local Search | DS2, April 21, 2020 in APB E005 | File 1, File 2, File 3 |
| Exercise | Tutorial 1 (Local Search) | DS1, April 23, 2020 in APB E005 | File 1, File 2, File 3 |
| Practical | Practical Work Topic | DS2, April 28, 2020 in APB E005 | File 1, File 2 |
| Lecture | Tabu Search | DS2, April 28, 2020 in APB E005 | File 1, File 2 |
| Exercise | Tutorial 2 (Tabu Search) | DS1, April 30, 2020 in APB E005 | File 1, File 2, File 3, File 4 |
| Lecture | ASP 1 | DS2, May 5, 2020 in APB E005 | File 1, File 2, File 3 |
| Exercise | Tutorial 3 (ASP 1) | DS1, May 7, 2020 in APB E005 | File 1, File 2 |
| Lecture | ASP 2 | DS2, May 12, 2020 in APB E005 | File 1, File 2, File 3 |
| Exercise | Tutorial 4 (ASP 2) | DS1, May 14, 2020 in APB E005 | File |
| Practical | Practical Work Discussion | DS1, May 15, 2020 in APB E005 | |
| Lecture | ASP 3 | DS2, May 19, 2020 in APB E005 | File 1, File 2, File 3 |
| Lecture | CSP | DS2, May 26, 2020 in APB E005 | File 1, File 2 |
| Exercise | Tutorial 5 (CSP) | DS1, May 28, 2020 in APB E005 | File 1, File 2 |
| Lecture | Evolutionary Algorithms | DS2, June 9, 2020 in APB E005 | File 1, File 2 |
| Exercise | Tutorial 6 (EA 1) | DS1, June 11, 2020 in APB E005 | File 1, File 2 |
| Lecture | Structural Decompositions 1 | DS2, June 16, 2020 in APB E005 | File 1, File 2 |
| Exercise | Tutorial 7 (EA 2) | DS1, June 18, 2020 in APB E005 | |
| Lecture | Structural Decompositions 2 | DS2, June 23, 2020 in APB E005 | File 1, File 2 |
| Exercise | Tutorial 8 (Structural Decompositions) | DS1, June 25, 2020 in APB E005 | File 1, File 2 |
| Practical | Practical Work Presentations | DS2, June 30, 2020 in APB E005 | |
| Exercise | Tutorial 9 (Structural Decompositions 2) | DS1, July 2, 2020 in APB E005 | File |
| Exercise | Tutorial 10 (Structural Decompositions 3) | DS1, July 9, 2020 in APB E005 | File |
| Lecture | Q&A | DS2, July 16, 2020 in APB E005 |
Calendar
