Problem Solving and Search in Artificial Intelligence

Lehrveranstaltung mit SWS 2/1/1 (Vorlesung/Übung/Praktikum) in SS 2015

Dozent

Sarah Alice Gaggl

Umfang (SWS)

2/1/1

Module

Leistungskontrolle

Mündliche Prüfung
Hausarbeit

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.

Veranstaltungskalender abonnieren (icalendar)

Vorlesung	Introduction	DS3, 14. April 2015 in APB E005	Datei
Vorlesung	Uninformed vs. Informed Search	DS3, 28. April 2015 in APB E005	Datei
Übung	Tutorial	DS2, 5. Mai 2015 in APB E005	Datei
Vorlesung	Local Search, Stochastic Hill Climbing, Simulated Annealing	DS3, 5. Mai 2015 in APB E005	Datei
Vorlesung	Tabu Search	DS3, 12. Mai 2015 in APB E005	Datei
Übung	Tutorial	DS2, 19. Mai 2015 in APB E005	Datei
Vorlesung	ASP I	DS3, 19. Mai 2015 in APB E005	Datei
Übung	Tutorial	DS2, 2. Juni 2015 in APB E005	Datei
Vorlesung	ASP II	DS3, 2. Juni 2015 in APB E005	Datei
Vorlesung	ASP III	DS3, 9. Juni 2015 in APB E005	Datei
Übung	Tutorial	DS2, 16. Juni 2015 in APB E005	Datei 1, Datei 2
Vorlesung	CSP	DS3, 16. Juni 2015 in APB E005	Datei
Übung	Tutorial	DS2, 23. Juni 2015 in APB E005	Datei
Vorlesung	Evolutionary Algorithms	DS3, 23. Juni 2015 in APB E005	Datei
Vorlesung	Structural Decomposition Techniques I	DS3, 30. Juni 2015 in APB E005	Datei
Übung	Tutorial	DS2, 7. Juli 2015 in APB E005	Datei
Vorlesung	Structural Decomposition Techniques II	DS3, 7. Juli 2015 in APB E005	Datei
Praktikum	Practical Work	DS2, 14. Juli 2015 in APB E005	Datei
Vorlesung	Q&A	DS3, 14. Juli 2015 in APB E005

Kalender