Problem Solving and Search in Artificial Intelligence

Problem Solving and Search in Artificial Intelligence

Course with SWS 2/1/1 (lecture/exercise/practical) in SS 2015

Lecturer

• Sarah Alice Gaggl

SWS

• 2/1/1

Modules

• INF-BAS2
• MCL-PI
• MCL-KR
• INF-VERT2

Examination method

• Oral exam
• Term paper

• Information
• Literature
• Dates and Materials

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
• Structural Decomposition Techniques (Tree/Hypertree Decompositions)
• Evolutionary Algorithms, Genetic Algorithms

Learning Outcomes

• The students should identify why typical AI problems are difficult to solve
• 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

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.

• 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 14, 2015 in APB E005	File
Lecture	Uninformed vs. Informed Search	DS3, April 28, 2015 in APB E005	File
Exercise	Tutorial	DS2, May 5, 2015 in APB E005	File
Lecture	Local Search, Stochastic Hill Climbing, Simulated Annealing	DS3, May 5, 2015 in APB E005	File
Lecture	Tabu Search	DS3, May 12, 2015 in APB E005	File
Exercise	Tutorial	DS2, May 19, 2015 in APB E005	File
Lecture	ASP I	DS3, May 19, 2015 in APB E005	File
Exercise	Tutorial	DS2, June 2, 2015 in APB E005	File
Lecture	ASP II	DS3, June 2, 2015 in APB E005	File
Lecture	ASP III	DS3, June 9, 2015 in APB E005	File
Exercise	Tutorial	DS2, June 16, 2015 in APB E005	File 1,  File 2
Lecture	CSP	DS3, June 16, 2015 in APB E005	File
Exercise	Tutorial	DS2, June 23, 2015 in APB E005	File
Lecture	Evolutionary Algorithms	DS3, June 23, 2015 in APB E005	File
Lecture	Structural Decomposition Techniques I	DS3, June 30, 2015 in APB E005	File
Exercise	Tutorial	DS2, July 7, 2015 in APB E005	File
Lecture	Structural Decomposition Techniques II	DS3, July 7, 2015 in APB E005	File
Practical	Practical Work	DS2, July 14, 2015 in APB E005	File
Lecture	Q&A	DS3, July 14, 2015 in APB E005

Calendar