FI:IA175 Quantitative Verification - Course Information
IA175 Algorithms for Quantitative Verification
Faculty of InformaticsAutumn 2024
- Extent and Intensity
- 2/1/1. 4 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
- Teacher(s)
- prof. Dr. rer. nat. RNDr. Mgr. Bc. Jan Křetínský, Ph.D. (lecturer)
- Guaranteed by
- prof. Dr. rer. nat. RNDr. Mgr. Bc. Jan Křetínský, Ph.D.
Department of Computer Science – Faculty of Informatics
Supplier department: Department of Computer Science – Faculty of Informatics - Timetable
- Tue 17. 9. to Tue 10. 12. Tue 10:00–11:50 B410
- Timetable of Seminar Groups:
- Prerequisites
- IB005
acquaintance with basic probability theory - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 39 fields of study the course is directly associated with, display
- Course objectives
- The course introduces
(1) several fundamental mathematical structures for modelling dynamic systems, where quantities such as probability, time, or cost are essential, and
(2) algorithms for their analysis, in particular their verification with respect to typical types of correctness requirements.
Besides, the course offers also a more practical experience with modelling and analysis tools. - Learning outcomes
- The student can:
- model systems and their properties in appropriate mathematical formalisms
- can analyze the systems with respect to the properties using the discussed algorithms
- can choose appropriate algorithms for the analysis
- can design modifications of these algorithms and can rigorously argue about their correctness, complexity, and (dis)advantages - Syllabus
- Motivation: verification, temporal logics, quantitative systems
- Timed automata: modelling, semantics; reachability, region construction; zones, timed CTL
- Markov chains: reachability, rewards, probabilistic LTL and CTL
- Markov decision processes: modelling, semantics; reachability (linear programming, value iteration, strategy iteration; interval iteration, bounded real-time dynamic programming), rewards, probabilistic LTL and CTL; reinforcement learning and approximate dynamic programming; multi-objective optimization
- Stochastic games: reachability (quadratic programing, value iteration, strategy iteration)
- Systems with continuous time and space
- Literature
- recommended literature
- BAIER, Christel and Joost-Pieter KATOEN. Principles of model checking. Cambridge, Mass.: MIT Press, 2008, xvii, 975. ISBN 9780262026499. info
- not specified
- MEYN, S. P. and R. L. TWEEDIE. Markov chains and stochastic stability. 2nd ed. Cambridge: Cambridge University Press, 2009, xxviii, 59. ISBN 9780521731829. info
- Algorithmic game theory. Edited by Noam Nisan. Cambridge: Cambridge University Press, 2007, xxi, 754. ISBN 9780521872829. info
- PUTERMAN, Martin L. Markov decision processes : discrete stochastic dynamic programming. Hoboken, N.J.: Wiley-Interscience, 2005, xvii, 649. ISBN 0471727822. info
- FILAR, Jerzy A. and Koos VRIEZE. Competitive Markov decision processes : with 57 illustrations. New York: Springer, 1997, xii, 393. ISBN 0387948058. info
- NORRIS, J. R. Markov chains. 1st pub. Cambridge: Cambridge University Press, 1997, xvi, 237. ISBN 9780521481816. info
- PUTERMAN, Martin L. Markov decision processes : discrete stochastic dynamic programming. New York: Wiley, 1994, xvii, 649. ISBN 0471619779. info
- Teaching methods
- lectures, excercises, projects, homework, flipped classrooms
- Assessment methods
- exam + homework/project
- Language of instruction
- English
- Further Comments
- The course is taught annually.
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/autumn2024/IA175