Course syllabus
7.5 hp, Study Period 1, HT26
The course is offered by the Department of Electrical Engineering
Contact details
Examiner and lecturer
Bengt Lennartson, phone: 0730-79 42 26, bengt.lennartson@chalmers.se
Teaching assistants
Lasse Kötz, kotz@chalmers.se (Please message me by email directly instead of Canvas messages.)
Ilya Kuangaliyev, ilyaku@chalmers.se
Exam Office
Room EDIT 3342, studadm.e2@chalmers.se
Course purpose
The course aims to provide fundamental knowledge and skills in logic, learning, and decision-making, especially modeling and specification formalisms, simulation, synthesis, optimization, and implementing control functions. Typical applications are control functions for embedded systems, control of automated production systems, and communication systems.
Schedule
Course literature
Logic, Learning, and Decision, Bengt Lennartson. Lecture Notes 2023, to be downloaded from Files.
Logic, Learning, and Decision - Exercises, 2023, to be downloaded from Files.
Lecture Program
| Lecture nr/ Book chapter Period week |
Date, Room | Contents |
|
L1, Ch. 1 |
Monday, Aug 31 |
Introduction. Discrete states, automata, typical models from different application areas, and closed-loop systems. Synchronous composition, specification, verification, controller synthesis, implementation. |
|
L2, Ch. 2 |
Thursday, Sept 3 |
Discrete mathematics. Propositional logic, truth tables, tautological equivalences, and implications. Formal proofs. |
|
L3, Ch. 2 |
Monday, Sept 7 |
Discrete mathematics. Sets, operations on sets, set algebra. Relations and fixed points. Satisfiability solvers. |
|
L4, Ch. 3 |
Thursday, Sept 10 |
Formal models. Automata, sets of states and events, transition relations, partial transition functions, traces, and formal languages. |
|
L5, Ch. 3 |
Monday, Sept 14 |
Formal models. Synchronous composition and language intersection, Petri nets. |
|
L6, Ch. 4, 6 |
Thursday, Sept 17 |
Modeling & Specification. Verification. Specification of desired and non-desired behaviors, marked, forbidden, and reachable states. Controllable and uncontrollable events, verification of controllability. |
|
L7, Ch. 7 |
Monday, Sept 21 |
Controller synthesis. Plant, specification, supervisor synthesis. |
|
L8, Ch. 7 |
Thursday, Sept 24 |
Temporal logic and mu calculus |
|
Pw 5 |
Monday, Sept 28 |
No lecture |
|
L9, Ch. 9 |
Thursday, Oct 1 |
Temporal logic and automata. |
|
L10 |
Monday, Oct 5 |
Reinforcement learning. |
|
L11, Ch. 8 |
Thursday, Oct 8 |
Extended models. Extended finite automata (EFAs), timed, and hybrid automata. |
|
L12, Ch. 8 |
Monday, Oct 12 |
Extended models. Markov chains. Queuing theory, Markov decision processes. |
|
L13 |
Thursday, Oct 15 |
Model reduction. Abstraction by Bisimulation. |
|
L14 |
Monday, Oct 19 |
Summary. Comments on the written examination. |
Exercises
Students are expected to spend significant time outside of class to solve all the problems. Solutions to the exercises are provided for additional support.
| Period week | Date, Room | Exercises |
|
Pw 1 |
Thursday, Sept 3 |
Introduction 1.1 - 1.8 Discrete mathematics 2.1 - 2.3 |
|
Pw 2 |
Thursday, Sept 10 |
Discrete mathematics 2.4 - 2.6 |
|
Pw 3 |
Thursday, Sept 17 |
Verification 6.1 - 6.6 |
|
Pw 4 |
Thursday, Sept 24 |
Controller synthesis 7.1 - 7.7 |
|
Pw 5 |
Thursday, Oct 1 |
Temporal Logic, old exams 2017.5, 2020.5, 2021.4, 2022.4b, 2023.3, 2024.5, 2025.4 |
|
Pw 6 |
Thursday, Oct 8 |
EFAs 8.1, old exams 2017.3, 2020.3 Reinforcement Learning, old exams 2020.6, 2021.5, 2022.5, 2023.4, 2024.6, 2025.5 |
|
Pw 7 |
Thursday, Oct 15 |
Markov processes 8.3, old exams 2017.6, 2023.5, 2025.6 Model reduction, old exams 2021.6, 2022.6, 2023.6 |
|
Pw 8 |
Thursday, Oct 22 |
Questions and preparations for the exam |
Exercise self-activity and support for home assignments
From period week two, a self-activity and support session for exercises and home assignments is offered on Wednesday, 8-10.
Home assignments
The course includes two mandatory home assignments and one optional introductory assignment. Students complete these activities in two-member groups. We strongly recommend completing the introductory assignment as preparation for the mandatory ones.
Changes made in the last years
Course name changed from Discrete Event Systems to Logic, Learning, and Decision. The Reinforcement Learning topic is extended to include continuous state-space models. The first two home assignments have been merged into one, so the course now includes only 2 home assignments.
Learning objectives and syllabus
After completion of this course, the student should be able to:
- Use basic discrete mathematics to be able to analyze discrete event systems.
- Give an account of different formalisms for modeling discrete event systems, especially finite state automata, formal languages, Petri nets, extended finite state machines, and timed and hybrid automata, and demonstrate skills to choose between them.
- Present different types of specifications, such as progress and safety specifications, defining what a system should and should not do.
- Compute and analyze different properties of discrete event systems such as reachability, coreachability, and controllability.
- Explain the meaning of supervisor synthesis, verification, and simulation.
- Use computer tools to synthesize and optimize control functions based on given system models and specifications for the desired behavior of the total closed-loop system.
- Formulate and analyze hybrid systems, including discrete and continuous dynamics.
- Specify temporal logic properties and verify them using mu-calculus.
- Explain and apply basic Markov processes and queuing theory for performance analysis of systems, including uncertainties.
- Apply reinforcement learning based on the dynamic programming principle.
Link to the syllabus on Studieportalen: Study plan
Examination form
Final grade requires an approved written examination and two approved home assignments (Assignments 1 and 2).
The regular examination date is October 24, am, and the first re-sit examination date is January 7, am. Allowed aids at the examination: Standard mathematical tables such as Beta.
Course representatives
The following students have been elected by the student administration to be course representatives in the course evaluation:
noa.andreasen@gmail.com Noa Andreasen
tilda.liljegren@icloud.com Tilda Liljegren
denizn@chalmers.se Deniz Namlisesli
gauransh.shama10@gmail.com Gauransh Sharma
zhiyu_xi1229@163.com Zhiyu Xi
As a study representative, you will be involved in the course evaluation process. See more details at https://www.chalmers.se/en/education/your-studies/plan-and-conduct-your-studies/course-evaluation/
Course summary:
| Date | Details | Due |
|---|---|---|