After successful completion of the course, participants are able to:
- work on cutting-edge research that combines machine learning, artificial intelligence with the rigorous techniques of model checking
- recognize situations in which the applications of model checking techniques for specification and analysis may be useful,
- explain the basic theory and algorithms of model checking for finite state and probabilistic automata,
- model (critical parts of) realistic computer-based systems as networks of automata,
- formalize desired properties of these systems in terms of automata or temporal logic, and
- use state-of-the-art tools for their analysis.
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As our daily lives depend increasingly on digital systems, the reliability of these systems becomes a crucial concern, and as the complexity of the systems grows, their reliability can no longer be sufficiently controlled by traditional approaches of testing and simulation. It becomes essential to build mathematical models of these systems, and to use (algorithmic) verification methods to analyse these models. In model checking, specifications about the system are expressed as (temporal) logic formulas, and efficient symbolic algorithms are used to traverse the model defined by the system and check if the specification holds or not. As model checking is fully automatic, requiring no complex knowledge of proof strategies or biases of simulation tools, it is the method of choice for industry-scale verification.
This course introduces several variants of model checking, and techniques and tools in particular:
- Explicit-state and symbolic algorithms for model checking linear-time (LTL) and branching-time (CTL) temporal logics for finite machines
- Symbolic model checking using BDDs
- Bisimulation abstraction techniques
- Model checking in machine learning applications
- PCTL for Markov chains and Markov decision processes
- Abstraction techniques for probabilistic model checking
- Model checking tools (Storm and PRISM)
- Applications of model checking for analysis of distributed algorithms and for fault-tree analysis
Instructional Modes
- Lecture
- Tutorial
- Self-study
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| Some knowledge of automata theory; probability theory; computational complexity. |
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Grades will be awarded on the basis of an exam (50%) and homework assignments/project (50%), with a minimum grade of 5,5 in both.
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| Depending on the interest of the students, requirements imposed by homework assignments, and recent scientific developments, the specific topics covered in this course may vary slightly from year to year. |
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