Problem Solving and Search in Artificial Intelligence

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Problem Solving and Search in Artificial Intelligence

Lehrveranstaltung mit SWS 2/1/1 (Vorlesung/Übung/Praktikum) in SS 2017

Dozent

Umfang (SWS)

  • 2/1/1

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

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.

Veranstaltungskalender abonnieren (icalendar)

Vorlesung Introduction DS4, 7. April 2017 in APB E005 Download
Vorlesung CSP DS4, 21. April 2017 in APB E005 Download
Praktikum Practical Work DS5, 25. April 2017 in APB E007 Download
Vorlesung Uninformed vs. Informed Search DS4, 28. April 2017 in APB E005 Download
Vorlesung Local Search, Hill Climbing, Simulated Annealing DS4, 5. Mai 2017 in APB E005 Download
Übung Tutorial 1 DS5, 9. Mai 2017 in APB E007 Download
Vorlesung Tabu Search DS4, 12. Mai 2017 in APB E005 Download
Übung Tutorial 2 DS5, 16. Mai 2017 in APB E007 Download
Vorlesung ASP 1 DS4, 19. Mai 2017 in APB E005 Download
Übung Tutorial 3 DS5, 23. Mai 2017 in APB E007 Download
Vorlesung ASP 2 DS4, 26. Mai 2017 in APB E005 Download
Praktikum Practical Work DS5, 30. Mai 2017 in APB E007
Vorlesung ASP 3 DS4, 2. Juni 2017 in APB E005
Übung Tutorial 4 DS5, 13. Juni 2017 in APB E007 Download
Vorlesung Evolutionary Algorithms DS4, 23. Juni 2017 in APB E005 Download
Vorlesung Structural Decompositions DS4, 30. Juni 2017 in APB E005 Download
Übung Tutorial 5 DS5, 4. Juli 2017 in APB E007 Download
Vorlesung Summary, Q&A, Practical Work DS4, 7. Juli 2017 in APB E005
Übung Tutorial 6, Practical Work DS5, 11. Juli 2017 in APB E007


Kalender

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