Advanced Problem Solving and Search

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Advanced Problem Solving and Search

Lehrveranstaltung mit SWS 2/2/0 (Vorlesung/Übung/Praktikum) in WS 2021

Dozent

Tutor

Umfang (SWS)

  • 2/2/0

Module

Leistungskontrolle

  • Mündliche Prüfung



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)

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.

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 tutorials, the students will analyze different problems and develop solutions for them.

Prerequisites

  • Basic knowledge of theoretical computer science and Logic.
  • Good English skills: both the teaching 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 Mondays DS1 (07.30 - 09.00 am) and the tutorials on Mondays DS2 (09.20 - 10.50 am). Please check the schedule for changes. The lectures will consist of videos and other materials that will be uploaded online. The tutorials will be online live sessions via BigBlueButton (no recordings of these sessions will be uploaded).

The slides of the lectures and exercises of the tutorials will be uploaded in this page, in each corresponding session. The lecture videos will be uploaded in OPAL and I 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/32014761984/CourseNode/101501861298687
  • 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.

Veranstaltungskalender abonnieren (icalendar)

Vorlesung Introduction DS1, 18. Oktober 2021 in Screencast
Vorlesung Informed vs. Uninformed Search DS1, 25. Oktober 2021 in Screencast
Vorlesung Local Search DS1, 1. November 2021 in Screencast
Übung Tutorial 1 (Local Search) DS2, 1. November 2021 in Screencast
Vorlesung Tabu Search DS1, 8. November 2021 in Screencast
Übung Tutorial 2 (Tabu Search) DS2, 8. November 2021 in Screencast
Vorlesung ASP 1 DS1, 15. November 2021 in Screencast
Übung Tutorial 3 (ASP 1) DS2, 15. November 2021 in Screencast
Vorlesung ASP 2 DS1, 22. November 2021 in Screencast
Übung Tutorial 4 (ASP 2) DS2, 22. November 2021 in Screencast
Vorlesung ASP 3 DS1, 29. November 2021 in Screencast
Vorlesung CSP DS1, 6. Dezember 2021 in Screencast
Übung Tutorial 5 (CSP) DS2, 6. Dezember 2021 in Screencast
Vorlesung Evolutionary Algorithms DS1, 13. Dezember 2021 in Screencast
Übung Tutorial 6 (EA 1) DS2, 13. Dezember 2021 in Screencast
Vorlesung Structural Decompositions 1 DS1, 10. Januar 2022 in Screencast
Übung Tutorial 7 (Structural Decompositions) DS2, 10. Januar 2022 in Screencast Datei
Vorlesung Structural Decompositions 2 DS1, 17. Januar 2022 in Screencast
Übung Tutorial 8 (Q&A) DS2, 17. Januar 2022 in Screencast
Vorlesung Q&A DS1, 24. Januar 2022 in Screencast


Kalender