Version vom 16. März 2023, 19:25 Uhr

Advanced Problem Solving and Search

Lehrveranstaltung mit SWS 2/2/0 (Vorlesung/Übung/Praktikum) in SS 2023

Dozent

Tutor

Umfang (SWS)

2/2/0

Module

Leistungskontrolle

Klausur
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
Constraint Satisfaction Problems
Evolutionary Algorithms, Genetic Algorithms
Answer Set Programming
Structural Decomposition Techniques (Tree/Hypertree Decompositions)

The course does not cover topics in the area of Machine Learning and Neural Networks.

NOTE: This course was previously named Problem Solving and Search in Artificial Intelligence. The contents of former PSSAI and APSS are identical, and therefore students can only take one of the two courses.

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 tutorials, the students will analyze different problems and develop solutions for them.

Prerequisites

Basic knowledge of theoretical computer science and Logic.
Good English skills: both the teaching and examination will be exclusively in English.

Organisation

The goals can be acquired by studying the lecture material and solving the exercises of the tutorials.

The slides of the lectures and exercises of the tutorials will be uploaded in OPAL, for each corresponding session. We invite you to use the forum to ask questions and share your exercise solutions.

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, 17. April 2023 in HSZ/0401
Vorlesung	ASP 1	DS2, 24. April 2023 in HSZ/0401
Übung	Tutorial 1 (ASP)	DS1, 28. April 2023 in APB E009
Übung	Tutorial 1 (ASP)	DS1, 8. Mai 2023 in APB E005
Vorlesung	ASP 2	DS2, 8. Mai 2023 in HSZ/0401
Übung	Tutorial 2 (ASP)	DS1, 12. Mai 2023 in APB E009
Übung	Tutorial 2 (ASP)	DS1, 15. Mai 2023 in APB E005
Vorlesung	ASP 3	DS2, 15. Mai 2023 in HSZ/0401
Vorlesung	Informed vs. Uninformed Search	DS2, 22. Mai 2023 in HSZ/0401
Vorlesung	Local Search	DS2, 5. Juni 2023 in HSZ/0401
Übung	Tutorial 3 (Local Search)	DS1, 9. Juni 2023 in APB E009
Übung	Tutorial 3 (Local Search)	DS1, 12. Juni 2023 in APB E005
Vorlesung	Tabu Search	DS2, 12. Juni 2023 in HSZ/0401
Übung	Tutorial 4 (Tabu Search)	DS1, 16. Juni 2023 in APB E009
Übung	Tutorial 4 (Tabu Search)	DS1, 19. Juni 2023 in APB E005
Vorlesung	Evolutionary Algorithms	DS2, 19. Juni 2023 in HSZ/0401
Übung	Tutorial 5 (EA)	DS1, 23. Juni 2023 in APB E009
Übung	Tutorial 5 (EA)	DS1, 26. Juni 2023 in APB E005
Vorlesung	CSP	DS2, 26. Juni 2023 in HSZ/0401
Übung	Tutorial 6 (CSP)	DS1, 30. Juni 2023 in APB E009
Übung	Tutorial 6 (CSP)	DS1, 3. Juli 2023 in APB E005
Vorlesung	Structural Decompositions 1	DS2, 3. Juli 2023 in HSZ/0401
Übung	Tutorial 7 (Structural Decompositions)	DS1, 7. Juli 2023 in APB E009
Übung	Tutorial 7 (Structural Decompositions)	DS1, 10. Juli 2023 in APB E005
Vorlesung	Structural Decompositions 2	DS2, 10. Juli 2023 in HSZ/0401
Vorlesung	Q&A	DS2, 17. Juli 2023 in HSZ/0401

Kalender

@@ Zeile 123: / Zeile 123: @@
 {{Vorlesung Zeiten
 |Lehrveranstaltungstype=Übung
-|Title=Tutorial 4 (Local Search)
+|Title=Tutorial 3 (Local Search)
 |Room=APB E005
 |Date=2023/06/12
@@ Zeile 130: / Zeile 130: @@
 {{Vorlesung Zeiten
 |Lehrveranstaltungstype=Übung
-|Title=Tutorial 4 (Local Search)
+|Title=Tutorial 3 (Local Search)
 |Room=APB E005
 |Date=2023/06/09
@@ Zeile 144: / Zeile 144: @@
 {{Vorlesung Zeiten
 |Lehrveranstaltungstype=Übung
-|Title=Tutorial 5 (Tabu Search)
+|Title=Tutorial 4 (Tabu Search)
 |Room=APB E005
 |Date=2023/06/19
@@ Zeile 151: / Zeile 151: @@
 {{Vorlesung Zeiten
 |Lehrveranstaltungstype=Übung
-|Title=Tutorial 5 (Tabu Search)
+|Title=Tutorial 4 (Tabu Search)
 |Room=APB E005
 |Date=2023/06/16

Advanced Problem Solving and Search (SS2023): Unterschied zwischen den Versionen