Problem Solving and Search in Artificial Intelligence
From International Center for Computational Logic
Problem Solving and Search in Artificial Intelligence
Course with SWS 2/1/1 (lecture/exercise/practical) in SS 2017
Lecturer
SWS
- 2/1/1
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
Basice 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 lecture is scheduled for Friday DS4 13:00-14:30 in room APB E005 and the tutorial will be held on Tuesday DS5 14:50-16:20 in room APB E007 (see the exact schedule). The practical work should be performed in groups of two students throughout the semester with regular updates on the progress.
The first lecture will be on Friday 7th April 2017, DS4 13:00-14:30 in room APB E005.
Note: the lecture on Friday 16th June 2017 will be held in room APB 2026.- 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 | DS4, April 7, 2017 in APB E005 | File |
| Lecture | CSP | DS4, April 21, 2017 in APB E005 | File |
| Practical | Practical Work | DS5, April 25, 2017 in APB E007 | File |
| Lecture | Uninformed vs. Informed Search | DS4, April 28, 2017 in APB E005 | File |
| Lecture | Local Search, Hill Climbing, Simulated Annealing | DS4, May 5, 2017 in APB E005 | File |
| Exercise | Tutorial 1 | DS5, May 9, 2017 in APB E007 | File |
| Lecture | Tabu Search | DS4, May 12, 2017 in APB E005 | File |
| Exercise | Tutorial 2 | DS5, May 16, 2017 in APB E007 | File |
| Lecture | ASP 1 | DS4, May 19, 2017 in APB E005 | File |
| Exercise | Tutorial 3 | DS5, May 23, 2017 in APB E007 | File |
| Lecture | ASP 2 | DS4, May 26, 2017 in APB E005 | File |
| Practical | Practical Work | DS5, May 30, 2017 in APB E007 | |
| Lecture | ASP 3 | DS4, June 2, 2017 in APB E005 | |
| Exercise | Tutorial 4 | DS5, June 13, 2017 in APB E007 | File |
| Lecture | Evolutionary Algorithms | DS4, June 23, 2017 in APB E005 | File |
| Lecture | Structural Decompositions | DS4, June 30, 2017 in APB E005 | File |
| Exercise | Tutorial 5 | DS5, July 4, 2017 in APB E007 | File |
| Lecture | Summary, Q&A, Practical Work | DS4, July 7, 2017 in APB E005 | |
| Exercise | Tutorial 6, Practical Work | DS5, July 11, 2017 in APB E007 |
Calendar
