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 2019

Lecturer

Tutor

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

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.
  • 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 DS3, April 8, 2019 in APB E005 Download
Practical Practical Work DS2, April 15, 2019 in APB E005 Download
Lecture CSP DS3, April 15, 2019 in APB E005 Download
Lecture Uninformed vs. Informed Search DS2, April 29, 2019 in APB 1004 Download
Lecture Local Search, Hill Climbing, Simulated Annealing DS3, April 29, 2019 in APB 1004 Download
Exercise Tutorial 1 (CSP) DS2, May 6, 2019 in HSZ/003/H Download
Lecture Tabu Search DS3, May 6, 2019 in HSZ/003/H Download
Exercise Tutorial 2 (Search) DS2, May 13, 2019 in MER/02/H Download
Lecture ASP 1 DS3, May 13, 2019 in MER/02/H Download
Exercise Tutorial 3 (ASP 1) DS2, May 20, 2019 in MER/02/H Download
Lecture ASP 2 DS3, May 20, 2019 in MER/02/H Download
Practical Practical Work DS2, May 27, 2019 in MER/02/H
Lecture ASP 3 DS3, May 27, 2019 in MER/02/H
Exercise Tutorial 4 (ASP 2) DS2, June 3, 2019 in MER/02/H
Lecture Evolutionary Algorithms DS3, June 3, 2019 in MER/02/H Download
Exercise Tutorial 5 (Evolutionary Algorithms) DS2, June 17, 2019 in MER/02/H
Lecture Structural Decompositions 1 DS3, June 17, 2019 in MER/02/H Download
Exercise Tutorial 6 (Structural Decompositions) DS2, June 24, 2019 in MER/02/H Download
Lecture Structural Decompositions 2 DS3, June 24, 2019 in MER/02/H Download
Lecture Summary, Q&A, Practical Work DS3, July 1, 2019 in MER/02/H
Practical Practical Work DS2, July 8, 2019 in MER/02/H
Practical Practical Work DS3, July 8, 2019 in MER/02/H


Calendar