module english
DESCRIPTION
3rfw4tTRANSCRIPT
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Module Handbook for Master of Science Software Systems Engineering
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2 Revision: 13.06.2013 02:26:16
(Index of Contents)
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Degree Course Information: Master of Science Software Systems Engineering [MSSSE/11]
Title Master of Science Software Systems Engineering
Short Title SSE (M.Sc.) Link to Further Information
http://dbis.rwth-aachen.de/SSE/
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4 Revision: 13.06.2013 02:26:16
Module: Network Algorithms [MSSSE-1101101/11] Module Title Network Algorithms
Short Title Network Algorithms
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content
Routing algorithms for interconnected parallel computers Sorting networks Randomized methods for contention resolution and congestion avoidance
Algorithms for wireless networks Data management in networks Theory of peer-to-peer networks
Aims and Learning Outcomes
Knowledge about the theory of algorithms for computer networks Ability to model and analyze algorithmic problems arising in computer networks Knowledge about fundamental algorithmic design principles like randomized contention
resolution and congestion avoidance
Prerequisites Basic knowledge about algorithms, discrete structures and probability theory
Course Texts
Zur Vorlesung gibt es ein Skript. Empfohlene Bcher
F.T. Leighton. Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Computer Networking: A Top-Down Approach Featuring the Internet. Addison Wesley
Longman, 1999.
Language of Instruction Englisch
Module Coordinator Berthold Vcking
Credits 6
Contact Hours per week 4
Self-Study Time (h) 120 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Network Algorithms MSSSE-1101101.a/11
0 4 3 75
Exercise Network Algorithms
MSSSE-1101101.b/11
0 2 1 45
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5
Masterexam Network Algorithms
MSSSE-1101101.c/11
6 0 0 0
Assessment: Lecture Network Algorithms [MSSSE-1101101.a/11] Title Lecture Network Algorithms
Short Title Lecture Network Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Network Algorithms [MSSSE-1101101.b/11] Title Exercise Network Algorithms
Short Title Exercise Network Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Network Algorithms [MSSSE-1101101.c/11] Title Masterexam Network Algorithms
Short Title Masterexam Network Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
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6 Revision: 13.06.2013 02:26:16
Module: Algorithmic Game Theory [MSSSE-1101102/11] Module Title Algorithmic Game Theory
Short Title Algorithmic Game Theory
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Start of Cycle Variable
Content
Introduction to game theory Complexity of game theoretic solution concepts Congestion and potential games Price of anarchy Algorithmic aspects of mechanism design
Aims and Learning Outcomes
Knowledge of basic game theoretic solution concepts and their complexity Critical understanding of the basic game theoretical assumptions Ability to model problems using game theoretic approaches for the design and analysis of
algorithms and networks
Prerequisites Basic knowledge about algorithms, discrete structures, probability theory (stochastic) Course Texts
N. Nisan, T. Roughgarden, E. Tardos, V. Vazirani. Algorithmic Game Theory, Cambridge University Press, 2007. T. Roughgarden. Selfish Routing and the Price of Anarchy. MIT Press, 2005. A. Mas-Colell, M.D. Whinston, and J.R. Green. Microeconomic Theory. Oxford University
Press, 1995. M.J. Osborne. An Introduction to Game Theory. Oxford University Press. 2004.
Language of Instruction Englisch
Module Coordinator Berthold Vcking
Credits 6
Contact Hours per week 4
Self-Study Time (h) 120 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Algorithmic Game Theory
MSSSE-1101102.a/11
0 4 3 75
Exercise Algorithmic Game Theory
MSSSE-1101102.b/11
0 2 1 45
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7
Masterexam Algorithmic Game Theory
MSSSE-1101102.c/11
6 0 0 0
Assessment: Lecture Algorithmic Game Theory [MSSSE-1101102.a/11] Title Lecture Algorithmic Game Theory
Short Title Lecture Algorithmic Game Theory
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Algorithmic Game Theory [MSSSE-1101102.b/11] Title Exercise Algorithmic Game Theory
Short Title Exercise Algorithmic Game Theory
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Algorithmic Game Theory [MSSSE-1101102.c/11] Title Masterexam Algorithmic Game Theory
Short Title Masterexam Algorithmic Game Theory
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
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8 Revision: 13.06.2013 02:26:16
Module: Algorithmic Cryptography [MSSSE-1101103/11] Module Title Algorithmic Cryptography
Short Title Algorithmic Cryptography
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Course Texts Zur Vorlesung wird ein Skript erstellt und folgende Literatur empfohlen:
H. Delfs, H. Knebl: Introduction to Cryptography. Springer 2002 A. Salomaa: Public-Key Cryptography. Springer 1996. F.L. Bauer: Entzifferte Geheimnisse. Springer 2000.
Language of Instruction Englisch
Module Coordinator Walter Unger
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecure Algorithmic Cryptography
MSSSE-1101103.a/11
0 4 3 75
Exercise Algorithmic Cryptography
MSSSE-1101103.b/11
0 2 2 30
Masterexam Algorithmic Cryptography
MSSSE-1101103.c/11
6 0 0 0
Assessment: Lecure Algorithmic Cryptography [MSSSE-1101103.a/11] Title Lecure Algorithmic Cryptography
Short Title Lecture Algorithmic Cryptography
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Algorithmic Cryptography [MSSSE-1101103.b/11] Title Exercise Algorithmic Cryptography
Short Title Exercise Algorithmic Cryptography
Semester of Study 1
Content see module description
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9
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Algorithmic Cryptography [MSSSE-1101103.c/11] Title Masterexam Algorithmic Cryptography
Short Title Masterexam Algorithmic Cryptography
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
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10 Revision: 13.06.2013 02:26:16
Module: Graph Algorithms [MSSSE-1101104/11] Module Title Graph Algorithms
Short Title Graph Algorithms
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Course Texts Zur Vorlesung wird ein Skript erstellt und folgende Literatur empfohlen:
Golumbic M.C. Algorithmic Graph Theory and Perfect Graphs Harary F.: Graphentheorie, 1974. Wilson R.J.: Einfhrung in die Graphentheorie, 1972
Language of Instruction Englisch
Module Coordinator Walter Unger
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Graph Algorithms MSSSE-1101104.a/11
0 4 3 75
Exercise Graph Algorithms MSSSE-1101104.b/11
0 2 2 30
Masterexam Graph Algorithms
MSSSE-1101104.c/11
6 0 0 0
Assessment: Lecture Graph Algorithms [MSSSE-1101104.a/11] Title Lecture Graph Algorithms
Short Title Lecture Graph Algorithms
Semester of Study 1
Content see module decsription
Relevance to Degree Programme
Degree elective
Assessment: Exercise Graph Algorithms [MSSSE-1101104.b/11] Title Exercise Graph Algorithms
Short Title Exercise Graph Algorithms
Semester of Study 1
Content see module description
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11
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Graph Algorithms [MSSSE-1101104.c/11] Title Masterexam Graph Algorithms
Short Title Masterexam Graph Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
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12 Revision: 13.06.2013 02:26:16
Module: Theoretical Foundations of Distributed Systems [MSSSE-1101106/11] Module Title Theoretical Foundations of Distributed Systems
Short Title Theoretical Foundations of Distributed Systems
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
2
Content
Routing in networks: centralized and distributed approaches Randomized methods for contention resolution and congestion avoidance
Queueing Theory (stochastic and adversarial) Game theoretic models (esp. congestion games) Distributed Hash Tables Peer-2-Peer Networks (Chord) Wireless networks (Yao graph, broadcasting, SINR model)
Aims and Learning Outcomes
Knowledge about the theoretical foundations of distributed systems with a focus on algorithmic problems and solutions Ability to model algorithmic problems arising in distributed systems Knowledge about fundamental algorithmic design principles like randomized contention
resolution and congestion avoidance
Prerequisites Basic knowledge about algorithms, discrete structures, and probability theory
Course Texts
Folien und Skripte Empfohlene Bcher Leighton. Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Kurose, Ross: Computer Networking: A Top-Down Approach Featuring the Internet.
Addison Wesley Longman, 1999. Kleinberg, Tardos: Algorithm Design, Addison Wesley Pearson, 2005
Language of Instruction English
Module Coordinator Berthold Vcking
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Theory of Distributed Systems
MSSSE-1101106.a/11
0 4 3 75
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13
Exercise Theory of Distributed Systems
MSSSE-1101106.b/11
0 2 2 30
Masterexam Theory of Distributed Systems
MSSSE-1101106.c/11
6 0 0 0
Assessment: Lecture Theory of Distributed Systems [MSSSE-1101106.a/11] Title Lecture Theory of Distributed Systems
Short Title Lecture Theory of Distributed Systems
Semester of Study 1
Relevance to Degree Programme
Degree elective
Assessment: Exercise Theory of Distributed Systems [MSSSE-1101106.b/11] Title Exercise Theory of Distributed Systems
Short Title Exercise Theory of Distributed Systems
Semester of Study 1
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Theory of Distributed Systems [MSSSE-1101106.c/11] Title Masterexam Theory of Distributed Systems
Short Title Masterexam Theory of Distributed Systems
Semester of Study 1
Relevance to Degree Programme
Degree elective
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Module: Analysis of Algorithms [MSSSE-1101201/11] Module Title Analysis of Algorithms
Short Title Analysis of Algorithms
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content In this lecture, you learn about basic techniques for the analysis of algorithms and apply them on numerous examples. On one hand, some algorithms will be analyzed in great detail -- culminating in the exact number of machine instructions executed on average --, on the other hand you learn how to get asymptotic estimates of the running time with very little effort.
Aims and Learning Outcomes
Decomposing algorithms into their basic blocks and findingvrecurrence relations for the number of times they are executed Elementary methods for the solution of these recurrence relations Mathematical techniques for the analysis of algorithms, in particular generating functions,
singularity analysis, and saddle point method Gaining experience in the analysis of algorithms by applying all these methods on
numerous practical examples
Prerequisites
Knowledge of probability theory and basic calculus Knowledge in the field of efficient algorithms
Course Texts Lecture Notes on Analysis of Algorithms and the books
R. Sedgewick and P. Flajolet. An Introduction to the Analysis of Algorithms. R. Sedgewick and P. Flajolet. Analytic Combinatorics.
Language of Instruction Englisch
Module Coordinator Peter Rossmanith
Credits 8
Contact Hours per week 6
Self-Study Time (h) 150 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Analysis of Algorithms
MSSSE-1101201.a/11
0 6 4 120
Exercise Analysis of Algorithms
MSSSE-1101201.b/11
0 2 2 30
Masterexam Analysis of Algorithms
MSSSE-1101201.c/11
8 0 0 0
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15
Assessment: Lecture Analysis of Algorithms [MSSSE-1101201.a/11] Title Lecture Analysis of Algorithms
Short Title Lecture Analysis of Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Analysis of Algorithms [MSSSE-1101201.b/11] Title Exercise Analysis of Algorithms
Short Title Exercise Analysis of Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Analysis of Algorithms [MSSSE-1101201.c/11] Title Masterexam Analysis of Algorithms
Short Title Masterexam Analysis of Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
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16 Revision: 13.06.2013 02:26:16
Module: Parameterized Algorithms [MSSSE-1101202/11] Module Title Parameterized Algorithms
Short Title Parameterized Algorithms
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content Many practical problems turned out to be NP-hard and in the classical view they are therefore ``intractable.''
Parameterized algorithms aim at exploiting that practical instances are not as hard as worst case instances. In this lecture, you will learn about such parameterized algorithms and general techniques for their design. We emphasize those techniques that lead to algorithms that are useful in practice. There are also some techniques that easily show that some problems can indeed be solved by a parameterized algorithm, but the running times will be very high. Finally, there are techniques that show that certain problems probably cannot have parameterized algorithms at all based on a complexity theory for parameterized problems.
Aims and Learning Outcomes
Knowledge of the most important parameterized algorithms and techniques for their design Ability to design efficient parameterized algorithms for decision and optimization problems Basic knowledge of parameterized complexity theory and the ability to show that certain
problems probably cannot be solved by parameterized algorithms
Prerequisites Knowledge in Efficient Algorithms
Course Texts
R. Downey and M. Fellows. Parameterized Algorithms. R. Niedermeier. Invitation to Fixed-Parameter Algorithms. J. Flum and M. Grohe. Parameterized Complexity Theory.
Language of Instruction Englisch
Module Coordinator Peter Rossmanith
Credits 8
Contact Hours per week 6
Self-Study Time (h) 150 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Parameterized Algorithms
MSSSE-1101202.a/11
0 6 4 120
Exercise Parameterized Algorithms
MSSSE-1101202.b/11
0 2 2 30
Masterexam Parameterized Algorithms
MSSSE-1101202.c/11
8 0 0 0
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17
Assessment: Lecture Parameterized Algorithms [MSSSE-1101202.a/11] Title Lecture Parameterized Algorithms
Short Title Lecture Parameterized Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Parameterized Algorithms [MSSSE-1101202.b/11] Title Exercise Parameterized Algorithms
Short Title Exercise Parameterized Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Parameterized Algorithms [MSSSE-1101202.c/11] Title Masterexam Parameterized Algorithms
Short Title Masterexam Parameterized Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
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18 Revision: 13.06.2013 02:26:16
Module: Exact Algorithms [MSSSE-1101203/11] Module Title Exact Algorithms
Short Title Exact Algorithms
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Start of Cycle Variable
Content
An introduction into exact algorithms fr NP-hard problems, e.g., Branching Dynamic Programming Inclusion-exclusion Measure & Conquer Subset Convolution
Aims and Learning Outcomes
Ability to develop fast exact algorithms for hard problems
Prerequisites Suggested: Efficient Algorithms
Course Texts Aktuelle Verffentlichungen
Language of Instruction Englisch
Module Coordinator Peter Rossmanith
Credits 8
Contact Hours per week 6
Self-Study Time (h) 150 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Exact Algorithms MSSSE-1101203.a/11
0 6 4 120
Exercise Exact Algorithms MSSSE-1101203.b/11
0 2 2 30
Masterexam Exact Algorithms
MSSSE-1101203.c/11
8 0 0 0
Assessment: Lecture Exact Algorithms [MSSSE-1101203.a/11] Title Lecture Exact Algorithms
Short Title Lecture Exact Algorithms
Semester of Study 1
Content see module description
-
19
Relevance to Degree Programme
Degree elective
Assessment: Exercise Exact Algorithms [MSSSE-1101203.b/11] Title Exercise Exact Algorithms
Short Title Exercise Exact Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Exact Algorithms [MSSSE-1101203.c/11] Title Masterexam Exact Algorithms
Short Title Masterexam Exact Algorithms
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
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20 Revision: 13.06.2013 02:26:16
Module: Methods in Network Analysis [MSSSE-1101301/11] Module Title Methods in Network Analysis
Short Title Methods in Network Analysis
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content
Introduction to the analysis of social networks
Computational aspects of centrality measures
Random graph models, power laws
Computational aspects of clustering measures
Cascading Behavior, diffusion of information
Viral dynamics and viral marketing
Rumor spreading
Aims and Learning Outcomes
Critical understanding of fundamental modeling assumptions in the anaylsis of social networks
Knowledge of basic measures for clustering and centrality and their computational aspects
Knowledge of simple models for random graphs and their properties
Ability to mathematically model and analyze problems arising in the design of algorithms for social and information networks
Prerequisites Basic knowledge of algorithms, discrete structures and probability theory
Course Texts
D. Easley, J. Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, 2010 U. Brandes, T. Erlebach. Network Analysis. Springer Verlag, 2005 D. Wasserman, K. Faust. Social Network Analysis. Cambridge University Press, 1994
Language of Instruction Englisch
Module Coordinator Martin Hoefer
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105
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21
Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
MSSSE-1101301.a/11
0 4 3 75
MSSSE-1101301.b/11
0 2 2 30
MSSSE-1101301.c/11
6 0 0 0
Assessment: [MSSSE-1101301.a/11] Semester of Study 1
Relevance to Degree Programme
Degree elective
Assessment: [MSSSE-1101301.b/11] Semester of Study 1
Relevance to Degree Programme
Degree elective
Assessment: [MSSSE-1101301.c/11] Semester of Study 1
Relevance to Degree Programme
Degree elective
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22 Revision: 13.06.2013 02:26:16
Module: Model Cecking [MSSSE-1102101/11] Module Title Model Cecking
Short Title Model Checking
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
3
Content Main topics:
Transition systems Concurrent and channel systems Property classes: safety, liveness, invariants, and fairness
Linear Temporal Logic (LTL) Computation Tree Logic (CTL) Model Checking algorithms for LTL and (fair) CTL Abstraction: (Bi)simulation
Aims and Learning Outcomes
Acquisition of the following proficiencies:
Modeling of (concurrent) programs Knowledge of property classes Understanding the construction and functioning of model-checking algorithms for LTL and
CTL Understanding of elementary abstraction mechanisms Capability of employing Model Checkers (Spin)
Prerequisites
Knowledge of fundamental automata models and regular languages Knowledge of propositional logic Knowledge of basic data structures such as stacks, trees, and graphs and related
algorithms Basic knowledge of complexity theory
Course Texts Folien zur Vorlesung sowie folgende Lehrbcher: C. Baier, J.-P. Katoen: Principles of Model Checking, MIT Press, 2008. M. Huth and M.D. Ryan: Logic in Computer Science, Modelling and Reasoning about
Systems, Cambridge Univ. Press, 2004. E.M. Clarke, O. Grumberg, D. Peled: Model Checking, MIT Press, 1999.
Language of Instruction Englisch
Module Coordinator Joost-Pieter Katoen Wolfgang Thomas
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105
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23
Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Model Checking MSSSE-1102101.a/11
0 4 3 75
Exercise Model Checking MSSSE-1102101.b/11
0 2 2 30
Masterexam Model Checking
MSSSE-1102101.c/11
6 0 0 0
Assessment: Lecture Model Checking [MSSSE-1102101.a/11] Title Lecture Model Checking
Short Title Lecture Model Checking
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Exercise Model Checking [MSSSE-1102101.b/11] Title Exercise Model Checking
Short Title Exercise Model Checking
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Model Checking [MSSSE-1102101.c/11] Title Masterexam Model Checking
Short Title Masterexam Model Checking
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
-
24 Revision: 13.06.2013 02:26:16
Module: Compiler Construction [MSSSE-1102102/11] Module Title Compiler Construction
Short Title Compiler Construction
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
3
Content Main topics:
Lexical analysis of programs (scanner) Syntactic analysis of programs (parser) Semantic analysis of programs (attribute grammars) Generation of optimization of intermediate code Tools for compiler construction (lex, yacc)
Aims and Learning Outcomes
Acquisition of the following proficiencies:
Understanding the construction and functioning of compilers for higher-level programming languages Knowledge of using formal methods for syntax specification (regular expressions,
context-free and attribute grammars, EBNF) Capability of implementing simple compiler components (scanner, parser) Knowledge of using compiler-generating tools
Prerequisites
Understanding essential concepts of imperative and object-oriented programming languages and elementary programming techniques Knowledge of basic data structures such as lists, stacks, queues, and trees Knowledge of fundamental automata models such as finite and pushdown automata
Course Texts Folien und Skripte zur Vorlesung sowie folgende Lehrbcher:
A. Aho, R. Sethi, J. Ullman: Compilers -- Principles, Techniques, and Tools. Addison-Wesley, 1988. A.W. Appel, J. Palsberg: Modern Compiler Implementation in Java. Cambridge University
Press, 2002. D. Grune, H.E. Bal, C.J.H. Jacobs, K.G. Langendoen: Modern Compiler Design. Wiley &
Sons, 2000.
Language of Instruction Deutsch
Module Coordinator Thomas Noll Uwe Naumann
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
-
25
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Compiler Construction
MSSSE-1102102.a/11
0 4 3 75
Exercise Compiler Construction
MSSSE-1102102.b/11
0 2 2 30
Masterexam Compiler Construction
MSSSE-1102102.c/11
6 0 0 0
Assessment: Lecture Compiler Construction [MSSSE-1102102.a/11] Title Lecture Compiler Construction
Short Title Lecture Compiler Construction
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Exercise Compiler Construction [MSSSE-1102102.b/11] Title Exercise Compiler Construction
Short Title Exercise Compiler Construction
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Compiler Construction [MSSSE-1102102.c/11] Title Masterexam Compiler Construction
Short Title Masterexam Compiler Construction
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
-
26 Revision: 13.06.2013 02:26:16
Module: Advanced Model Checking [MSSSE-1102103/11] Module Title Advanced Model Checking
Short Title Advanced Model Checking
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
4
Content Main topics:
Abstraction: stutter (bi)simulation Partial-order reduction Binary Decision Diagrams Timed Automata Markov Chains and Decision Processes Timed and Probabilistic CTL Model Checking Probabilistic Processes
Aims and Learning Outcomes
Acquisition of the following proficiencies:
Fundamental knowledge of formal models for real-time systems Fundamental knowledge of quantitative extensions of CTL Understanding the functioning of Model Checking algorithms for Timed and Probabilistic
CTL
Prerequisites
Knowledge of elementary probability theory Fundamental knowledge of Model Checking techniques
Course Texts Folien zur Vorlesung sowie folgende Lehrbcher:
C. Baier, J.-P. Katoen: Principles of Model Checking, MIT Press, 2008. J. Rutten, M. Kwiatkowska, G. Norman and D. Parker: Mathematical Techniques for
Analyzing Concurrent and Probabilistic Systems, Volume 23 of CRM Monograph Series. American Mathematical Society, P. Panangaden and F. van Breugel (eds.), March 2004. M. Huth and M.D. Ryan: Logic in Computer Science -- Modelling and Reasoning about
Systems, Cambridge University Press, 2nd edition, 2004 E.M. Clarke, O. Grumberg, D.A. Peled: Model Checking, MIT Press, 1999 K.L. McMillan: Symbolic Model Checking, Kluwer Academic, 1993
Language of Instruction Englisch
Module Coordinator Joost-Pieter Katoen
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105
-
27
Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Advanced Model Checking
MSSSE-1102103.a/11
0 4 3 75
Exercise Advanced Model Checking
MSSSE-1102103.b/11
0 2 2 30
Masterexam Advanced Model Checking
MSSSE-1102103.c/11
6 0 0 0
Assessment: Lecture Advanced Model Checking [MSSSE-1102103.a/11] Title Lecture Advanced Model Checking
Short Title Lecture Advanced Model Checking
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Exercise Advanced Model Checking [MSSSE-1102103.b/11] Title Exercise Advanced Model Checking
Short Title Exercise Advanced Model Checking
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Advanced Model Checking [MSSSE-1102103.c/11] Title Masterexam Advanced Model Checking
Short Title Masterexam Advanced Model Checking
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
-
28 Revision: 13.06.2013 02:26:16
Module: Semantics and Verification of Software [MSSSE-1102104/11] Module Title Semantics and Verification of Software
Short Title Semantics and Verification of Software
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content
Introduction of WHILE model language Operational, denotational, and axiomatic semantics of WHILE Equivalence of operational and denotational semantics Dataflow analysis and abstract interpretation Abstraction and refinement
Aims and Learning Outcomes
Acquisition of the following proficiencies:
Understanding the fundamental concepts of formal semantics for imperative programming languages Capability of reasoning using formal derivation and proof systems Knowledge of basic techniques for program analysis Capability of applying formal concepts for proving compiler correctness Knowledge of using program analysis tools
Prerequisites
Understanding essential concepts of imperative and object-oriented programming languages and elementary programming techniques Knowledge of foundations of formal systems and automata theory Fundamental knowledge of mathematical logic
Course Texts Folien und Skripte zur Vorlesung sowie folgende Lehrbcher:
G. Winskel: The Formal Semantics of Programming Languages. MIT Press, 1993. F. Nielson, H.R. Nielson, C. Hankin: Principles of Program Analysis, 2nd ed., Springer,
2005. H.R. Nielson, F. Nielson: Semantics with Applications: A Formal Introduction, Wiley, 1992.
Language of Instruction Deutsch/Englisch
Module Coordinator Thomas Noll
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications
-
29
Title Ref. Code Credits Credits Workload
Contact hours (h)
Self-Study Time (h)
Lecture Semantics and Verification of Software
MSSSE-1102104.a/11
0 4 3 75
Exercise Semantics and Verification of Software
MSSSE-1102104.b/11
0 2 2 30
Masterexam Semantics and Verification of Software
MSSSE-1102104.c/11
6 0 0 0
Assessment: Lecture Semantics and Verification of Software [MSSSE-1102104.a/11] Title Lecture Semantics and Verification of Software
Short Title Lecture Semantics and Verification of Software
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Exercise Semantics and Verification of Software [MSSSE-1102104.b/11] Title Exercise Semantics and Verification of Software
Short Title Exercise Semantics and Verification of Software
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Semantics and Verification of Software [MSSSE-1102104.c/11] Title Masterexam Semantics and Verification of Software
Short Title Masterexam Semantics and Verification of Software
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
-
30 Revision: 13.06.2013 02:26:16
Module: Modeling Concurrent and Probabilistic Systems [MSSSE-1102105/11] Module Title Modeling Concurrent and Probabilistic Systems
Short Title Modeling Concurrent and Probabilistic Systems
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content Main topics:
Milner's Calculus of Communicating Systems (CCS) and its Semantics Equivalence of CCS Processes Case Study: the Alternating-Bit Protocol Stochastic Processes Probabilistic Process Algebra and its Semantics Equivalence of Probabilistic Processes Probabilities and Nondeterminism Markovian Process Algebra
Aims and Learning Outcomes
Acquisition of the following proficiencies:
Formal methods for modeling concurrent systems Fundamentals of Markov Chains Fundamentals of process algebras Understanding of probabilistiv process algebras Knowledge of definition and applications of equivalences for reducing state spaces
Prerequisites
Knowledge of fundamental automata models such as finite and pushdown automata Knowledge of elementary probability theory
Course Texts Folien und Skripte zur Vorlesung sowie folgende Lehrbcher:
R. Milner: Communicating and Mobile Systems: the pi-Calculus. Cambridge University Press, 1999 R. Milner: Communication and Concurrency. Prentice Hall, 1989
H.C. Tijms: A first course in stochastic models. Wiley, 2003 J. Bergstra, A. Ponse, S.A. Smolka: Handbook of Process Algebra. Elsevier, 2001
(Chapters 6 and 11) J. Hillston: A Compositional Approach to Performance Modelling. Cambridge University
Press, 1996 H. Hermanns: Interactive Markov Chains: The Quest for Quantified Quality. LNCS 2428,
Springer 2002 E. Brinksma, H. Hermanns, J.-P. Katoen: Lectures on Formal Methods and Performance
Analysis. LNCS 2090, Springer 2001
Language of Instruction Deutsch/Englisch
Module Coordinator Joost-Pieter Katoen
-
31
Thomas Noll
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Modeling Concurrent and Probabilistic Systems
MSSSE-1102105.a/11
0 4 3 75
Exercise Modeling Concurrent and Probabilistic Systems
MSSSE-1102105.b/11
0 2 2 30
Masterexam Modeling Concurrent and Probabilistic Systems
MSSSE-1102105.c/11
6 0 0 0
Assessment: Lecture Modeling Concurrent and Probabilistic Systems [MSSSE-1102105.a/11] Title Lecture Modeling Concurrent and Probabilistic Systems
Short Title Lecture Modeling Concurrent, Probabilistic Systems
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Exercise Modeling Concurrent and Probabilistic Systems [MSSSE-1102105.b/11] Title Exercise Modeling Concurrent and Probabilistic Systems
Short Title Exercise Modeling Concurrent , Probabilistic Syst.
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Modeling Concurrent and Probabilistic Systems [MSSSE-1102105.c/11] Title Masterexam Modeling Concurrent and Probabilistic Systems
Short Title Masteream Modeling Concurrent, Probabilistic Syst.
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
-
32 Revision: 13.06.2013 02:26:16
Module: Foundations of the UML [MSSSE-1102106/11] Module Title Foundations of the UML
Short Title Foundations of the UML
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content Main topics:
Sequence diagrams and their semantics Elementary properties of sequence diagrams High-level sequence graphs Communicating finite automata Realizability Statecharts and their semantics Object Constraint Language (OCL) and its semantics
Aims and Learning Outcomes
Acquisition of the following proficiencies:
Fundamental knowledge of UML diagrams Understanding of formal semantics of sequence diagrams and Statecharts Knowledge of the Object Constraint Language Capability of applying formal modelling techniques to software systems
Prerequisites
Knowledge of fundamental automata models such as finite and pushdown automata Fundamental knowledge of mathematical logic Knowledge of discrete mathematics Basic knowledge of complexity theory
Course Texts Folien und Skripte zur Vorlesung sowie folgende Lehrbcher:
Jos Warmer and Anneke Kleppe, Object Constraint Language, The: Precise Modeling with UML. Addison Wesley, 2001. D. Harel and M. Politi, Modeling Reactive Systems with Statecharts: The STATEMATE
Approach, McGraw-Hill, 1998.
Language of Instruction Deutsch/Englisch
Module Coordinator Joost-Pieter Katoen
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
-
33
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Foundations of the UML
MSSSE-1102106.a/11
0 4 3 75
Exercie Foundations of the UML
MSSSE-1102106.b/11
0 2 2 30
Masterexam Foundations of the UML
MSSSE-1102106.c/11
6 0 0 0
Assessment: Lecture Foundations of the UML [MSSSE-1102106.a/11] Title Lecture Foundations of the UML
Short Title Lecture Foundations of the UML
Semester of Study 1
Content see moduledecsription
Relevance to Degree Programme
Degree elective
Assessment: Exercie Foundations of the UML [MSSSE-1102106.b/11] Title Exercie Foundations of the UML
Short Title Exercise Foundations of the UML
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Foundations of the UML [MSSSE-1102106.c/11] Title Masterexam Foundations of the UML
Short Title Masterexam Foundations of the UML
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
-
34 Revision: 13.06.2013 02:26:16
Module: Testing of Reactive Systems [MSSSE-1102107/11] Module Title Testing of Reactive Systems
Short Title Testing of Reactive Systems
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content Main topics:
Groundwork: automata, labelled transitions systems, specification of processes Observation of processes Conformance of processes Derivation of test cases from transition systems Incorporating the quantitative notion of time into test cases Symbolic testing
Aims and Learning Outcomes
Acquisition of the following proficiencies:
Basic knowledge of how to describe behaviour, and how to distinguish it by observation In-depth knowledge of the prevalent theories for specification-based testing, in particular
for functional and timed testing Proficiency in proving simple theorems in the context of the lecture
Prerequisites Basic knowledge of finite automata theory
Course Texts Skript Testing of Reactive Systems---Course Notes, on-line erhltlich. Folgende Lehrbcher als ergnzende Literatur:
Manfred Broy, Bengt Jonsson, Joost-Pieter Katoen, Martin Leucker, Alexander Pretschner: Model-Based Testing of Reactive Systems (Advanced Lectures), Volume 3472 of Lecture Notes in Computer Science. Springer-Verlag, 2005
Language of Instruction Englisch
Module Coordinator Henrik Bohnenkamp Joost-Pieter Katoen
Credits 6
Contact Hours per week 4
Self-Study Time (h) 120 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Testing of Reactive Systems
MSSSE-1102107.a/11
0 4 3 75
Exercise Testing of Reactive Systems
MSSSE-1102107.b/11
0 2 1 45
-
35
Masterexam Testing of Reactive Systems
MSSSE-1102107.c/11
6 0 0 0
Assessment: Lecture Testing of Reactive Systems [MSSSE-1102107.a/11] Title Lecture Testing of Reactive Systems
Short Title Lecture Testing of Reactive Systems
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Exercise Testing of Reactive Systems [MSSSE-1102107.b/11] Title Exercise Testing of Reactive Systems
Short Title Exercise Testing of Reactive Systems
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Testing of Reactive Systems [MSSSE-1102107.c/11] Title Masterexam Testing of Reactive Systems
Short Title Masterexam Testing of Reactive Systems
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
-
36 Revision: 13.06.2013 02:26:16
Module: Static Program Analysis [MSSSE-1102109/11] Module Title Static Program Analysis
Short Title Static Program Analysis
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Course Texts
Flemming Nielson, Hanne R. Nielson, Chris Hankin: Principles of Program Analysis. 2. Ausgabe, Springer, 2005
Helmut Seidl, Reinhard Wilhelm, Sebastian Hack: Ubersetzerbau 3: Analyse und Transformation. Springer, 2009
Steven S. Muchnick, Neil D. Jones: Program Flow Analysis: Theory and Applications. Prentice Hall, 1981
Language of Instruction Deutsch/Englisch
Module Coordinator Thomas Noll
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Static Program Analysis
MSSSE-1102109.a/11
0 4 3 75
Exercise Static Program Analysis
MSSSE-1102109.b/11
0 2 2 30
Masterexam Static Program Analysis
MSSSE-1102109.c/11
6 0 0 0
Assessment: Lecture Static Program Analysis [MSSSE-1102109.a/11] Title Lecture Static Program Analysis
Short Title Lecture Static Program Analysis
Semester of Study 1
Relevance to Degree Programme
Degree elective
Assessment: Exercise Static Program Analysis [MSSSE-1102109.b/11] Title Exercise Static Program Analysis
Semester of Study 1
Relevance to Degree Programme
Degree elective
-
37
Assessment: Masterexam Static Program Analysis [MSSSE-1102109.c/11] Title Masterexam Static Program Analysis
Short Title Masterexam Static Program Analysis
Semester of Study 1
Relevance to Degree Programme
Degree elective
-
38 Revision: 13.06.2013 02:26:16
Module: Concurrency Theory [MSSSE-1102110/11] Module Title Concurrency Theory
Short Title Concurrency Theory
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
4
Content Main topics:
1. Introduction 2. The Interleaving Approach 3. The True Concurrency Approach 4. Refinement and Compositionality 5. Case Studies and Tools 6. Extensions
Aims and Learning Outcomes
Acquire the knowledge and competences to: understand the foundations of concurrent systems model (and compare) concurrent systems in a rigorous manner
understand the main semantical underpinnings of concurrency
Prerequisites
Knowledge of fundamental automata models (Course Formale Systeme, Automaten und Prozesse) Understanding of the working principles of parallel and distributed systems (Courses
Betriebssysteme und Systemsoftware and Systemprogrammierung)
Course Texts Folien und Skripte zur Vorlesung sowie folgende Lehrbcher:
Luca Aceto, Anna Inglfsdttir, Kim Guldstrand Larsen and Jiri Srba: Reactive Systems: Modelling, Specification and Verification. Cambridge University Press, 2007. Maurice Herlihy and Nir Shavit: The Art of Multiprocessor Programming. Elsevier, 2008. Jan Bergstra, Alban Ponse and Scott Smolka (Eds.): Handbook of Process Algebra.
Elsevier, 2001. Wolfgang Reisig: Understanding Petri Nets: Modeling Techniques, Analysis Methods,
Case Studies. Springer Verlag, 2012.
Language of Instruction Deutsch/Englisch
Module Coordinator Joost-Pieter Katoen Thomas Noll
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
-
39
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Concurrency Theory MSSSE-1102110.a/11
0 4 3 75
Exercise Concurrency Theory
MSSSE-1102110.b/11
0 2 2 30
Exam Concurrency Theory MSSSE-1102110.c/11
6 0 0 0
Assessment: Lecture Concurrency Theory [MSSSE-1102110.a/11] Title Lecture Concurrency Theory
Short Title Lecture Concurrency Theory
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Concurrency Theory [MSSSE-1102110.b/11] Title Exercise Concurrency Theory
Short Title Exercise Concurrency Theory
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exam Concurrency Theory [MSSSE-1102110.c/11] Title Exam Concurrency Theory
Short Title Exam Concurrency Theory
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
-
40 Revision: 13.06.2013 02:26:16
Module: Term Rewrite Systems [MSSSE-1102201/11] Module Title Term Rewrite Systems
Short Title Term Rewrite Systems
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content basics
syntax of equations semantics of equations
term rewriting
equational reasoning congruence closure term rewrite systems
termination of term rewriting
decidability results reduction relations simplification orders and recursive path orders
confluence of term rewriting
local confluence critical pairs
completion of term rewrite systems
Knuth-Bendix completion implicit induction
Aims and Learning Outcomes
learning how to use term rewrite techniques in all areas that require symbolic computation with equations
learning how to use term rewrite techniques for the specification, analysis, and verification of programs. In particular, term rewrite techniques can be used to
analyze whether programs are deterministic analyze whether programs terminate analyze whether programs are correct complete programs and specifications that are incomplete
Prerequisites
first basic knowledge on functional programming would be advantageous, but is not required (lecture Programming Concepts) first basic knowledge on predicate logic would beadvantageous, but is not required (lecture
Mathematical Logic)
-
41
Course Texts Skript und Folien zur Vorlesung sowie z.B. folgende Bcher:
J. Avenhaus: Reduktionssysteme, Springer, 1995. F. Baader, T. Nipkow: Term Rewriting and All That, Cambridge University Press, 1998. R. Bndgen: Termersetzungssysteme, Vieweg, 1998. E. Ohlebusch: Advanced Topics in Term Rewriting, Springer, 2002 Terese: Term Rewriting Systems, Cambridge University Press, 2003.
Language of Instruction Deutsch/Englisch
Module Coordinator Jrgen Giesl
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Term Rewrite Systems
MSSSE-1102201.a/11
0 4 3 75
Exercise Term Rewrite Systems
MSSSE-1102201.b/11
0 2 2 30
Masterexam Term Rewrite Systems
MSSSE-1102201.c/11
6 0 0 0
Assessment: Lecture Term Rewrite Systems [MSSSE-1102201.a/11] Title Lecture Term Rewrite Systems
Short Title Lecture
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Term Rewrite Systems [MSSSE-1102201.b/11] Title Exercise Term Rewrite Systems
Short Title Exercise Term Rewrite Systems
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Term Rewrite Systems [MSSSE-1102201.c/11] Title Masterexam Term Rewrite Systems
Short Title Masterexam Term Rewrite Systems
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42 Revision: 13.06.2013 02:26:16
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
-
43
Module: Logic Programming [MSSSE-1102202/11] Module Title Logic Programming
Short Title Logic Programming
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content basics of predicate logic
unification resolution Horn clauses and SLD-resolution
logic programs
operational and denotational semantics evaluation strategies
the programming language Prolog
negation-as-failure non-logical components of Prolog programming techniques
applications and extensions of logic programming Aims and Learning Outcomes
learning the programming techniques in logic languages knowledge of the concepts and the logical foundations of logic languages
learning how to formally define the semantics of logic programming languages learning how to implement logic languages learning how to use logic languages in different application areas
Prerequisites
basic programming concepts (lecture Programming Concepts) first basic knowledge on logic programming would be advantageous, but is not required
(lecture Programming Concepts) first basic knowledge on predicate logic would be advantageous, but is not required
(lecture Mathematical Logic)
Course Texts Skript und Folien zur Vorlesung sowie z.B. folgende Bcher:
I. Bratko: Prolog Programming for Artificial Intelligence, Addison-Wesley, 2001. W. F. Clocksin, C. S. Mellish: Programming in Prolog, Springer, 2003. T. Frwirth, S. Abdennadher: Essentials of Constraint Programming, Springer, 2003. M. Hanus: Problemlsen mit Prolog, Teubner, 1987. J. W. Lloyd: Foundations of Logic Programming, Springer, 1987. P. H. Schmitt: Theorie der logischen Programmierung, Springer, 1992. L. Sterling, E. Shapiro: The art of Prolog, MIT Press, 2000.
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44 Revision: 13.06.2013 02:26:16
Language of Instruction Deutsch/Englisch
Module Coordinator Jrgen Giesl
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Logic Programming MSSSE-1102202.a/11
0 4 3 75
Exercise Logic Programming
MSSSE-1102202.b/11
0 2 2 30
Masterexam Logic Programming
MSSSE-1102202.c/11
6 0 0 0
Assessment: Lecture Logic Programming [MSSSE-1102202.a/11] Title Lecture Logic Programming
Short Title Lecture Logic Programming
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Logic Programming [MSSSE-1102202.b/11] Title Exercise Logic Programming
Short Title Exercise Logic Programming
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Logic Programming [MSSSE-1102202.c/11] Title Masterexam Logic Programming
Short Title Masterexam Logic Programming
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
-
45
Module: Functional Programming [MSSSE-1102203/11] Module Title Functional Programming
Short Title Functional Programming
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content introduction to the programming language Haskell
syntax of the different language constructs higher-order functions programming with lazy evaluation monads
denotational semantics of functional programs
complete partial orders and fixpoints denotational semantics of Haskell
lambda calculus
syntax and operational semantics of the lambda calculus reducing Haskell to the lambda calculus
type checking and inference Aims and Learning Outcomes
learning the programming techniques in functional languages knowledge of the foundational concepts behind functional languages learning how to formally define the semantics of functional programming languages learning how to implement functional languages learning how to develop type checking techniques for functional languages
Prerequisites
basic programming concepts (lecture Programming Concepts) first basic knowledge on functional programming would be advantageous, but is not
required (lecture Programming Concepts)
Course Texts Skript und Folien zur Vorlesung sowie z.B. folgende Bcher:
R. Bird: Introduction to Functional Programming Using Haskell, Prentice Hall, 1998. G. Hutton: Programming in Haskell, Cambridge University Press, 2007.
B. O'Sullivan, D. Stewart, J. Goerzen: Real World Haskell, O'Reilly, 2008. P. Pepper: Funktionale Programmierung, Springer, 2002. C. Reade: Elements of Functional Programming, Addison-Wesley, 1989. P. Thiemann: Grundlagen der Funktionalen Programmierung, Teubner, 1994.
Language of Instruction Deutsch/Englisch
Module Coordinator Jrgen Giesl
-
46 Revision: 13.06.2013 02:26:16
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Functional Programming
MSSSE-1102203.a/11
0 4 3 75
Exercise Functional Programming
MSSSE-1102203.b/11
0 2 2 30
Masterexam Functional Programming
MSSSE-1102203.c/11
6 0 0 0
Assessment: Lecture Functional Programming [MSSSE-1102203.a/11] Title Lecture Functional Programming
Short Title Lecture Functional Programming
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Functional Programming [MSSSE-1102203.b/11] Title Exercise Functional Programming
Short Title Exercise Functional Programming
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Functional Programming [MSSSE-1102203.c/11] Title Masterexam Functional Programming
Short Title Masterexam Functional Programming
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
-
47
Module: Deductive Program Verification [MSSSE-1102204/11] Module Title Deductive Program Verification
Short Title Deductive Program Verification
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Content basics
many-sorted predicate logic relations syntax and semantics of functional programs
partial correctness
specifications proving correctness by induction
verification techniques for partial correctness
symbolic evaluation automation of induction proofs heuristics applying lemmata
verification techniques for termination analysis
termination proofs with reduction orders termination proofs with dependency pairs
Aims and Learning Outcomes
learning how to use automated reasoning techniques for program verification knowledge about automated techniques for automated induction proofs in order to verify
partial correctness of programs knowledge about methods for automated termination analysis of programs learning how to implement and optimize automated program verification techniques learning how to develop heuristics in order to improve the automation of verification
techniques
Prerequisites
first basic knowledge on functional programming would be advantageous, but is not required (lecture Programming Concepts) first basic knowledge on predicate logic would be advantageous, but is not required
(lecture Mathematical Logic)
Course Texts Skript und Folien zur Vorlesung sowie z.B. folgende Literatur:
T. Arts, J. Giesl: Termination of Term Rewriting Using Dependency Pairs, Theoretical Computer Science, 236:133-178, 2000. K. H. Blsius, H.-J. Brckert: Deduktionssysteme, Oldenbourg, 1992.
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48 Revision: 13.06.2013 02:26:16
A. Bundy: The Automation of Proof by Mathematical Induction, Handbook of Automated Reasoning, pages 845-911, Elsevier & MIT Press, 2001. C. Walther: Mathematical Induction, Handbook of Logic in Artificial Intelligence and Logic
Programming, Vol. 2, pages 127-227, Oxford University Press, 1994. C. Walther: Semantik und Programmverifikation, Teubner-Wiley, 2001.
Language of Instruction Deutsch/Englisch
Module Coordinator Jrgen Giesl
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Deductive Program Verification
MSSSE-1102204.a/11
0 4 3 75
Exercise Deductive Program Verification
MSSSE-1102204.b/11
0 2 2 30
Masterexam Deductive Program Verification
MSSSE-1102204.c/11
6 0 0 0
Assessment: Lecture Deductive Program Verification [MSSSE-1102204.a/11] Title Lecture Deductive Program Verification
Short Title Lecture Deductive Program Verification
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Deductive Program Verification [MSSSE-1102204.b/11] Title Exercise Deductive Program Verification
Short Title Exercise Deductive Program Verification
Semester of Study 2
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Deductive Program Verification [MSSSE-1102204.c/11] Title Masterexam Deductive Program Verification
Short Title Masterexam Deductive Program Verification
Semester of Study 1
Content see module description
-
49
Relevance to Degree Programme
Degree elective
-
50 Revision: 13.06.2013 02:26:16
Module: Modeling and analysis of hybrid systems [MSSSE-1102301/11] Module Title Modeling and analysis of hybrid systems
Short Title Modeling and analysis of hybrid systems
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
2
Content Hybrid systems are systems with mixed discrete-continuous behaviour. They are everywhere. Physical systems with a discrete part in their control, like automobiles, aircrafts, and other transport systems, robots etc. are hybrid systems. But also software whose real-time behaviour is relevant can be seen as a hybrid system. Such systems play an important role in, e.g., CAD (Computer Aided Design), real-time software, robotics, process control, and computer-aided verification.
In the last years we can observe an intensive development in this area. New methodologies were developed to model such kind of systems and to analyse their behaviour. In this lecture we follow this development and deal with different aspects of hybrid systems, from their modeling to their verification.
Contents:
Discrete, continuous, and dynamic systems, hybrid systems, examples Modeling: Hybrid Automata Some important features: Determinism, blocking systems, Zeno-behaviour, stability
etc. Interesting classes of hybrid systems: Timed Automata, linear systems, non-linear
systems Analysis: Model Checking, deduction, abstraktion, simulation, testing Controller synthesis
Aims and Learning Outcomes
The lecture should teach the students how to model, specify, implement, and analyse real-time software systems or discrete controller for continuous systems.
Prerequisites None
Course Texts Wird in der Vorlesung bekannt gegeben.
Language of Instruction Deutsch/Englisch
Module Coordinator Erika Abraham
Credits 6
Contact Hours per week 4
Self-Study Time (h) 120 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Modeling and analysis of hybrid systems
MSSSE-1102301.a/11
0 4 3 75
Exercise Modeling and analysis of hybrid systems
MSSSE-1102301.b/11
0 2 1 45
-
51
Masterexam Modeling and analysis of hybrid systems
MSSSE-1102301.c/11
6 0 0 0
Assessment: Lecture Modeling and analysis of hybrid systems [MSSSE-1102301.a/11] Title Lecture Modeling and analysis of hybrid systems
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Exercise Modeling and analysis of hybrid systems [MSSSE-1102301.b/11] Title Exercise Modeling and analysis of hybrid systems
Short Title Exercise Modeling and analysis of hybrid systems
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Modeling and analysis of hybrid systems [MSSSE-1102301.c/11] Title Masterexam Modeling and analysis of hybrid systems
Short Title Masterexam Modeling and analysis of hybrid systems
Semester of Study 1
Content see moduledescription
Relevance to Degree Programme
Degree elective
-
52 Revision: 13.06.2013 02:26:16
Module: Satisfiability Checking [MSSSE-1102302/11] Module Title Satisfiability Checking
Short Title Satisfiability Checking
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
2
Content
Propositional logic, the satisfiability problem, satisfiability check (SAT-solving) Propositional logic with quantifiers (QBF-solving) First-order logic, theories Theory of equalities and uninterpreted functions, satisfiability check Theory of the reals with addition, satisfiability check (the Simplex method, the Branch and
Bound method, Fourier-Motzkin variable elimination) SAT-solving + Theory-solving: Satisfiability modulo theories (SMT-solving) Deduction, theorem proving Approximative methods Application: Bounded model checking (transition systems, expressing bounded
reachability, expressing safety and lifeness properties)
Aims and Learning Outcomes
The students should be able to formalize certain problems in an adequate logic/theory, and check the satisfiability of the resulting formula with the help of adequate algorithms. This way they can decide if the problem is solvable, and eventually determine a satisfying solution.
The following skills are attained: Problem formalization, application of satisfiability checking algorithms, especially for verification purposes.
Prerequisites As regarding contents, the following moduls are needed: Mathematical logic, as well as Algorithms and data structures.
Course Texts Folien der Vorlesung und die folgenden Bcher:
Daniel Kroening, Ofer Strichman: Decision Procedures: An Algorithmic Point of View. Springer Berlin, 2008 Aaron R. Bradley, Zohar Manna: The Calculus of Computation: Decision Procedures with
Applications to Verification. Springer, Berlin. 2007
Language of Instruction Deutsch oder Englisch
Module Coordinator Erika Abraham
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105
-
53
Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Satisfiability Checking
MSSSE-1102302.a/11
0 4.5 3 90
Exercises Satisfiability Checking
MSSSE-1102302.b/11
0 1.5 1 30
Masterexam Satisfiability Checking
MSSSE-1102302.c/11
6 0 0 0
Assessment: Lecture Satisfiability Checking [MSSSE-1102302.a/11] Title Lecture Satisfiability Checking
Short Title Lecture Satisfiability Checking
Semester of Study 5
Relevance to Degree Programme
Degree elective
Assessment: Exercises Satisfiability Checking [MSSSE-1102302.b/11] Title Exercises Satisfiability Checking
Short Title Exercises Satisfiability Checking
Semester of Study 5
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Satisfiability Checking [MSSSE-1102302.c/11] Title Masterexam Satisfiability Checking
Short Title Masterexam Satisfiability Checking
Semester of Study 5
Relevance to Degree Programme
Degree elective
-
54 Revision: 13.06.2013 02:26:16
Module: Applied Automata Theory [MSSSE-1107101/11] Module Title Applied Automata Theory
Short Title AAT
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
2
Content
Miminization of automata and bisimulation Learning of regular languages Weighted automata, including probabilistic automata Automata und logic Pushdown systems Undecidable problems in automata theory Petri nets
Aims and Learning Outcomes
Clear conception of basic state-based models in computer science Ability to assess models with respect to the fundamental properties of expressiveness and
algorithmic complexity
Prerequisites Courses 'Formal Systems, Automata, Processes', 'Computability and Complexity', 'Logic' of Bachelor Curriculum
Course Texts W. Thomas, Applied Automata Theory, Lecture Notes, RWTH Aachen
Language of Instruction Englisch
Module Coordinator Wolfgang Thomas
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Applied Automata Theory
MSSSE-1107101.a/11
0 4 3 75
Exercise Applied Automata Theory
MSSSE-1107101.b/11
0 2 2 30
Masterexam Applied Automata Theory
MSSSE-1107101.c/11
6 0 0 0
Assessment: Lecture Applied Automata Theory [MSSSE-1107101.a/11] Title Lecture Applied Automata Theory
Short Title Lecture Applied Automata Theory
-
55
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Applied Automata Theory [MSSSE-1107101.b/11] Title Exercise Applied Automata Theory
Short Title Exercise Applied Automata Theory
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Applied Automata Theory [MSSSE-1107101.c/11] Title Masterexam Applied Automata Theory
Short Title Mastereaxm Applied Automata Theory
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
-
56 Revision: 13.06.2013 02:26:16
Module: Infinite Games [MSSSE-1107102/11] Module Title Infinite Games
Short Title Infinite Games
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
2
Content
Graph-based games and the associated problem of solution Regular winning conditions for infinite games Solution of reachability games and Bchi games Solution of Muller games and parity games Application to automata on infinite trees Decidability of MSO-logic and other logics over infinite trees Outlook 1: Mean pay-off games Outlook 2: Games on infinite graphs, the Borel hierarchy
Aims and Learning Outcomes
Knowledge of infinite games as a model for reactive systems Understanding of the algorithmic content of the theory of infinite games Ability to apply game theoretic concepts and algorithms in logic as well as in the
verification and synthesis of systems
Prerequisites Courses of Theoretical Computer Science of Bachelor Curriculum Course 'Infinite Computations'
Course Texts W. Thomas, Automata and Reactive Systems, Lecture Notes, RWTH Aachen 2003
Language of Instruction Englisch
Module Coordinator Wolfgang Thomas
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Infinite Games MSSSE-1107102.a/11
0 4 3 75
Exercise Infinite Games MSSSE-1107102.b/11
0 2 2 30
Masterexam Infinite Games MSSSE-1107102.c/11
6 0 0 0
Assessment: Lecture Infinite Games [MSSSE-1107102.a/11] Title Lecture Infinite Games
-
57
Short Title Lecture Infinite Games
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Infinite Games [MSSSE-1107102.b/11] Title Exercise Infinite Games
Short Title Exercise Infinite Games
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Infinite Games [MSSSE-1107102.c/11] Title Masterexam Infinite Games
Short Title Masterexam Infinite Games
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
-
58 Revision: 13.06.2013 02:26:16
Module: Tree Automaton [MSSSE-1107103/11] Module Title Tree Automaton
Short Title Tree Automaton
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
4
Start of Cycle Variable
Content
Finite automata on ranked trees: bottom-up und top-down tree automata, closure properties and algorithms, regular expressions and grammars, connection to logic Finite automata on unranked trees: coding by ranked trees, connection to XML schema
languages Sequential automaton models: tree walking automata, XPath Tree transformations
Aims and Learning Outcomes
Understanding of the concept of finite automata on branching structures and their applications Ability to apply the automata theoretic view on schema languages for XML documents
Prerequisites Courses 'Formale Systeme, Automaten, Prozesse', 'Berechenbarkeit und Komplexitt', 'Mathematische Logik' of Bachelor Curriculum; Knowledge from the course 'Applied Automata Theory' is helpful but not required.
Course Texts Tree Automata: Techniques and Applications. Comon, Hubert; Dauchet, Max; Gilleron, Remi; Jacquemard, Florent; Lding, Christof and Lugiez, Denis; Tison, Sophie; Tommasi, Marc
Language of Instruction Deutsch/Englisch
Module Coordinator Wolfgang Thomas
Credits 4
Contact Hours per week 3
Self-Study Time (h) 75 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Tree Automaton MSSSE-1107103.a/11
0 2.5 2 45
Exercise Tree Automaton MSSSE-1107103.b/11
0 1.5 1 30
Masterexam Tree Automaton
MSSSE-1107103.c/11
4 0 0 0
Assessment: Lecture Tree Automaton [MSSSE-1107103.a/11] Title Lecture Tree Automaton
-
59
Short Title Lecture Tree Automaton
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Tree Automaton [MSSSE-1107103.b/11] Title Exercise Tree Automaton
Short Title Exercise Tree Automaton
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Tree Automaton [MSSSE-1107103.c/11] Title Masterexam Tree Automaton
Short Title Masterexam Tree Automaton
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
-
60 Revision: 13.06.2013 02:26:16
Module: Recursion Theory [MSSSE-1107104/11] Module Title Recursion Theory
Short Title Recursion Theory
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Course Texts
W. Thomas, Rekursionstheorie, Skript, RWTH Aachen 2004. N. Cutland, An Introduction to Recursive Function Theory, Cambridge Univ. Press 1980 H. Rogers, Theory of Recursive Functions and Effective Computability, McGrwa Hill 1967
Language of Instruction Deutsch
Module Coordinator Wolfgang Thomas
Credits 4
Contact Hours per week 3
Self-Study Time (h) 75 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Recursion Theory MSSSE-1107104.a/11
0 2.5 2 45
Exercise Recursion Theory MSSSE-1107104.b/11
0 1.5 1 30
Masterexam Recursion Theory
MSSSE-1107104.c/11
4 0 0 0
Assessment: Lecture Recursion Theory [MSSSE-1107104.a/11] Title Lecture Recursion Theory
Short Title Lecture Recursion Theory
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Recursion Theory [MSSSE-1107104.b/11] Title Exercise Recursion Theory
Short Title Exercise Recursion Theory
Semester of Study 1
Content see module description
-
61
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Recursion Theory [MSSSE-1107104.c/11] Title Masterexam Recursion Theory
Short Title Masterexam Recursion Theory
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
-
62 Revision: 13.06.2013 02:26:16
Module: Infinite Computations [MSSSE-1107105/11] Module Title Infinite Computations
Short Title Infinite Computations
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
2
Content Part I: Automata on infinite words 1. Bchi automata and regular omega-languages 2. Deterministic automata on infinite words 3. Classification of sequence properties (safety, recurrence, etc.) Part II: Applications 4. Decidability results on logical systems 5. Automata theoretic approach to model-checking 6. Algorithmic results on linear constraints for real numbers Part III: Outlook 7. Context-free omega-languages 8. The Borel hierarchy
Aims and Learning Outcomes
Clear conception of infinite objects in computer science and how algorithmic problems on them can be solved Acquaintance with the fundamentals of automata over infinite objects
Course Texts
W. Thomas, Automata and Reactive Systems, Lecture Notes, RWTH Aachen. D. Perrin, J.E. Pin, Infinite Words, Elsevier 2000.
Language of Instruction Englisch
Module Coordinator Wolfgang Thomas
Credits 6
Contact Hours per week 5
Self-Study Time (h) 105 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Infinite Computations
MSSSE-1107105.a/11
0 4 3 75
Exercise Infinite Computations
MSSSE-1107105.b/11
0 2 2 30
Masterexam Infinite Computations
MSSSE-1107105.c/11
6 0 0 0
Assessment: Lecture Infinite Computations [MSSSE-1107105.a/11] Title Lecture Infinite Computations
Short Title Lecture Infinite Computations
-
63
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Infinite Computations [MSSSE-1107105.b/11] Title Exercise Infinite Computations
Short Title Exercise Infinite Computations
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Infinite Computations [MSSSE-1107105.c/11] Title Masterexam Infinite Computations
Short Title Masterexam Infinite Computations
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
-
64 Revision: 13.06.2013 02:26:16
Module: Regular and Context-Free Languages: Advanced Results [MSSSE-1107106/11] Module Title Regular and Context-Free Languages: Advanced Results
Short Title RCL: Advanced Results
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
3
Content Part I: Regular Languages 1. Star-height and the star-height problem 2. Star-free languages, first-order logic and Schtzenberger's Theorem 3. Regular languages and circuit complexity Part II: Context-Free Languages 4. Chomsky-Schtzenberger Theorem 5. Generators of context-free, linear, and one counter-languages 6. Deterministic context-free languages
Aims and Learning Outcomes
Insight into the wide applicability of regular and context-free languages Knowledge of different viewpoints on these language classes, their classification
and algorithmic results.
Course Texts
H. Straubing, Finite Automata, Formal Logic, and Circuit Complexity, Birkhuser, Boston 1994. J. Berstel, Transductions and Context-Free Languages, Teubner, Stuttgart
M.A. Harrison, Introduction to Formal Language Theory, Addison-Wesley, Reading, Mass. 1978. W. Thomas, Lecture Notes on regular and context-free languages, RWTH Aachen
Module Coordinator Wolfgang Thomas
Credits 4
Contact Hours per week 3
Self-Study Time (h) 75 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
Lecture Regular and context-free languages: Advanced results
MSSSE-1107106.a/11
0 2.5 2 45
Exercise Regular and context-free languages: Advanced results
MSSSE-1107106.b/11
0 1.5 1 30
Masterexam Regular and context-free languages: Advanced results
MSSSE-1107106.c/11
4 0 0 0
Assessment: Lecture Regular and context-free languages: Advanced results [MSSSE-1107106.a/11]
-
65
Title Lecture Regular and context-free languages: Advanced results
Short Title Lecture RCL: Advanced Results
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Exercise Regular and context-free languages: Advanced results [MSSSE-1107106.b/11] Title Exercise Regular and context-free languages: Advanced results
Short Title Exercise RCL: Advanced Results
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
Assessment: Masterexam Regular and context-free languages: Advanced results [MSSSE-1107106.c/11] Title Masterexam Regular and context-free languages: Advanced results
Short Title Masterexam RCL: Advanced Results
Semester of Study 1
Content see module description
Relevance to Degree Programme
Degree elective
-
66 Revision: 13.06.2013 02:26:16
Module: [MSSSE-1107201/11] Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Course Texts
Folien zur Vorlesung sowie folgende Lehrbcher: Kearns, Vazirani: An Introduction to Computational Learning Theory, MIT Press, 1994
Fischer: Algorithmisches Lernen, Teubner, 1999
Language of Instruction Deutsch/Englisch
Module Coordinator Christof Lding
Credits 4
Contact Hours per week 3
Self-Study Time (h) 75 Relevance to Degree Programme
Degree elective
Assessment and Qualifications Title Ref. Code Credits Credits
Workload Contact hours (h)
Self-Study Time (h)
MSSSE-1107201.a/11
0 3 2 60
MSSSE-1107201.b/11
0 1 1 15
MSSSE-1107201.c/11
4 0 0 0
Assessment: [MSSSE-1107201.a/11] Semester of Study 1
Relevance to Degree Programme
Degree elective
Assessment: [MSSSE-1107201.b/11] Semester of Study 1
Relevance to Degree Programme
Degree elective
Assessment: [MSSSE-1107201.c/11] Semester of Study 1
Relevance to Degree Programme
Degree elective
-
67
Module: Complexity theory and quantum computing [MSSSE-1107301/11] Module Title Complexity theory and quantum computing
Short Title Complexity theory and quantum computing
Semester of Study 1
Duration (semesters) 1 Course Cycle (every n semesters)
0
Start of Cycle Variable
Content Deterministic, non-deterministic, parallel and probabilistic models of computations and associated complexity classes, complete problems, introduction to the mathematical and physical foundations of quantum computing, quantum bits and quantum registers, quantum gate arrays, important quantum algorithms, especially Shor's factorisation algorithm
Aims and Learning Outcomes
The students shall be enabled to classify problems according to their complexity. They shall become acquainted with the most important complexity classes for deterministic, non-deterministic, parallel and probabilistic models of computation and their relationship. Furthermore, the students shall become proficient in the foundations and important algorithms in quantum computing.
Prerequisites Successful completion of modules Mathematical Foundations, Linear