Problem Solving and Search in Artificial Intelligence

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Problem Solving and Search in Artificial Intelligence

Lehrveranstaltung mit SWS 2/2/2 (Vorlesung/Übung/Praktikum) in SS 2020

Dozent

Tutor

Umfang (SWS)

  • 2/2/2

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)


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.

Veranstaltungskalender abonnieren (icalendar)

Vorlesung Introduction DS2, 14. April 2020 in APB E005 Download Download Download
Vorlesung Informed vs. Uninformed Search DS1, 16. April 2020 in APB E005 Download Download
Vorlesung Local Search DS2, 21. April 2020 in APB E005 Download Download Download
Übung Tutorial 1 (Local Search) DS1, 23. April 2020 in APB E005 Download Download Download
Vorlesung Tabu Search DS2, 28. April 2020 in APB E005 Download Download
Praktikum Practical Work Topic DS2, 28. April 2020 in APB E005 Download Download
Übung Tutorial 2 (Tabu Search) DS1, 30. April 2020 in APB E005 Download Download Download Download
Vorlesung ASP 1 DS2, 5. Mai 2020 in APB E005 Download Download Download
Übung Tutorial 3 (ASP 1) DS1, 7. Mai 2020 in APB E005 Download Download
Vorlesung ASP 2 DS2, 12. Mai 2020 in APB E005 Download Download Download
Übung Tutorial 4 (ASP 2) DS1, 14. Mai 2020 in APB E005 Download
Praktikum Practical Work Discussion DS1, 15. Mai 2020 in APB E005
Vorlesung ASP 3 DS2, 19. Mai 2020 in APB E005 Download Download Download
Vorlesung CSP DS2, 26. Mai 2020 in APB E005 Download Download
Übung Tutorial 5 (CSP) DS1, 28. Mai 2020 in APB E005 Download Download
Vorlesung Evolutionary Algorithms DS2, 9. Juni 2020 in APB E005 Download Download
Übung Tutorial 6 (EA 1) DS1, 11. Juni 2020 in APB E005 Download Download
Vorlesung Structural Decompositions 1 DS2, 16. Juni 2020 in APB E005 Download Download
Übung Tutorial 7 (EA 2) DS1, 18. Juni 2020 in APB E005
Vorlesung Structural Decompositions 2 DS2, 23. Juni 2020 in APB E005 Download Download
Übung Tutorial 8 (Structural Decompositions) DS1, 25. Juni 2020 in APB E005 Download Download
Praktikum Practical Work Presentations DS2, 30. Juni 2020 in APB E005
Übung Tutorial 9 (Structural Decompositions 2) DS1, 2. Juli 2020 in APB E005 Download
Übung Tutorial 10 (Structural Decompositions 3) DS1, 9. Juli 2020 in APB E005 Download
Vorlesung Q&A DS2, 16. Juli 2020 in APB E005


Kalender