Advanced Problem Solving and Search
Advanced Problem Solving and Search
Course with SWS 2/2/0 (lecture/exercise/practical) in SS 2023
- Written exam
- 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
- Constraint Satisfaction Problems
- Evolutionary Algorithms, Genetic Algorithms
- Answer Set Programming
- Structural Decomposition Techniques (Tree/Hypertree Decompositions)
The course does not cover topics in the area of Machine Learning and Neural Networks.
NOTE: This course was previously named Problem Solving and Search in Artificial Intelligence. The contents of former PSSAI and APSS are identical, and therefore students can only take one of the two courses.
- 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 tutorials, the students will analyze different problems and develop solutions for them.
- Basic knowledge of theoretical computer science and Logic.
- Good English skills: both the teaching and examination will be exclusively in English.
The goals can be acquired by studying the lecture material and solving the exercises of the tutorials.
The slides of the lectures and exercises of the tutorials will be uploaded in OPAL, for each corresponding session. We invite you to use the forum to ask questions and share your exercise solutions.
Please, register for the course on the OPAL site: https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/39148716041
Key information: There are two forms of examination:
Students of the CMS master and exchange students:
- The written exam will take place on the 28th of July, 2023 at the TU Dresden campus. There is no possibility to take the exam remotely.
- You must register in Selma or with your examination office. In the future, you will also need to register on the OpalExam platform. A link will be provided for this.
Students with complex examinations:
- Please, schedule your oral exams with Ramona Behling (firstname.lastname@example.org).
- 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.
|Lecture||Introduction||DS2, April 17, 2023 in HSZ/0401|
|Lecture||ASP 1||DS2, April 24, 2023 in HSZ/0401|
|Exercise||Tutorial 1 (ASP)||DS1, April 28, 2023 in APB E009|
|Exercise||Tutorial 1 (ASP)||DS1, May 8, 2023 in APB E005|
|Lecture||ASP 2||DS2, May 8, 2023 in HSZ/0401|
|Exercise||Tutorial 2 (ASP)||DS1, May 12, 2023 in APB E009|
|Exercise||Tutorial 2 (ASP)||DS1, May 15, 2023 in APB E005|
|Lecture||ASP 3||DS2, May 15, 2023 in HSZ/0401|
|Lecture||Informed vs. Uninformed Search||DS2, May 22, 2023 in HSZ/0401|
|Lecture||Local Search||DS2, June 5, 2023 in HSZ/0401|
|Exercise||Tutorial 3 (Local Search)||DS1, June 9, 2023 in APB E009|
|Exercise||Tutorial 3 (Local Search)||DS1, June 12, 2023 in APB E005|
|Lecture||Tabu Search||DS2, June 12, 2023 in HSZ/0401|
|Exercise||Tutorial 4 (Tabu Search)||DS1, June 16, 2023 in APB E009|
|Exercise||Tutorial 4 (Tabu Search)||DS1, June 19, 2023 in APB E005|
|Lecture||Evolutionary Algorithms||DS2, June 19, 2023 in HSZ/0401|
|Exercise||Tutorial 5 (EA)||DS1, June 23, 2023 in APB E009|
|Exercise||Tutorial 5 (EA)||DS1, June 26, 2023 in APB E005|
|Lecture||CSP||DS2, June 26, 2023 in HSZ/0401|
|Exercise||Tutorial 6 (CSP)||DS1, June 30, 2023 in APB E009|
|Exercise||Tutorial 6 (CSP)||DS1, July 3, 2023 in APB E005|
|Lecture||Structural Decompositions 1||DS2, July 3, 2023 in HSZ/0401|
|Exercise||Tutorial 7 (Structural Decompositions)||DS1, July 7, 2023 in APB E009|
|Exercise||Tutorial 7 (Structural Decompositions)||DS1, July 10, 2023 in APB E005|
|Lecture||Structural Decompositions 2||DS2, July 10, 2023 in HSZ/0401|
|Lecture||Q&A||DS2, July 17, 2023 in HSZ/0401|