Course syllabus

7.5 hp, Study Period 1, HT24

 

The course is offered by the Department of Electrical Engineering

Contact details

Examiner and lecturer

Bengt Lennartson, phone: 031-772 3722, bengt.lennartson@chalmers.se

Teaching assistants

Lasse Kötz, kotz@chalmers.se
Alvin Combrink, combrink@chalmers.se

Exam Office

Room EDIT 3342, studadm.e2@chalmers.se

 

Course purpose

The course aims to give fundamental knowledge and skills in the area of logic, learning, and decision-making, especially modeling and specification formalisms, simulation, synthesis, optimization, and control function implementation. Typical applications are control functions for embedded systems, control of automated production systems, and communication systems.

 

Schedule

TimeEdit

 

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
Pw 1

Monday, Sept 2
15-17, HB2 

Introduction. Discrete states, automata, typical models from different application areas, and closed-loop systems. Synchronous composition, specification, verification, controller synthesis, implementation.

L2, Ch. 2
Pw 1

Thursday, Sept 5
8-10, HB1

Discrete mathematics. Propositional logic, truth tables, tautological equivalences, and implications. Formal proofs. 

L3, Ch. 2
Pw 2

Monday, Sept 9
13-16, HC4

Discrete mathematics. Sets, operations on sets, set algebra. Relations and fixed points. Satisfiability solvers.

L4, Ch. 3
Pw 2

Thursday, Sept 12
8-10, HB1

Formal models. Automata, sets of states and events, transition relations, partial transition functions, traces, and formal languages.

L5, Ch. 3
Pw 3

Monday, Sept 16
13-16, HC4

Formal models. Synchronous composition and language intersection, Petri nets.

L6,  Ch. 4, 6
Pw 3

Thursday, Sept 19
8-10, HB1

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
Pw 4

Monday, Sept 23
13-16, HC4

Controller synthesis. Plant, specification, supervisor synthesis.

L8, Ch. 7
Pw 4

Thursday, Sept 26
8-10, HB1

Controller synthesis. Supervisor synthesis algorithm.

L9. Ch. 9
Pw 5

Monday, Sept 30
13-16, HC4

Temporal logic and mu-calculus

L10, Ch. 9
Pw5

Thursday, Oct 3
8-10, HB1

Temporal logic and automata.

L11
Pw 6

Monday, Oct 7
13-16, HC4

Reinforcement learning.

L12, Ch. 8
Pw 6

Thursday, Oct 10
8-10, HB1

Extended models. Extended finite automata (EFAs), timed, and hybrid automata.

L13, Ch. 8
Pw 7

Monday, Oct 14
13-16, HC4

Extended models. Markov chains. Queuing theory, Markov decision processes.

L14
Pw 7

Thursday, Oct 17
8-10, HB2

Model reduction. Abstraction by Bisimulation.

L15
Pw 8

Thursday, Oct 24
10-12, SB-M500

Summary. Comments on the written examination.

 

Exercises

The student is expected to spend a significant amount of time besides these classes to solve all the problems. Solutions to the exercises are distributed to give additional support.

 

Period week Date, Room Exercises

   Pw 1

Thursday, Sept 5
10-12, HB1

Introduction 1.1 - 1.8
Discrete mathematics 2.1 - 2.3

   Pw 2

Thursday, Sept 12
10-12, HB1

Discrete mathematics 2.4 - 2.6
Formal models 3.1 - 3.5
Modeling and specification 4.1 - 4.9

   Pw 3

Thursday, Sept 19
10-12, HB1

Verification 6.1 - 6.6

   Pw 4

Thursday, Sept 26
10-12, HB1

Controller synthesis 7.1 - 7.7

   Pw 5

Thursday, Oct 3
10-12, HB1

Temporal Logic 17.5, 20.5, 21.4, 22.4b, 23.3

   Pw 6

Thursday, Oct 10
10-12, HB1

EFAs 8.1, 17.3, 20.3
Reinforcement Learning 20.6, 21.5, 22.5, 23.4

   Pw 7

Thursday, Oct 17
10-12, SB-H7

Markov processes 8.3, 17.6, 23.5
Model reduction 21.6, 22.6, 23.6

   Pw 8

Monday, Oct 21
13-15, HC4

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, SB-M022, except 25 Sept, 8-10, SB-L300.

 

Home assignments

Two mandatory home assignments, and one optional introductory assignment, are included in the course.  These activities are performed in two-member groups. We strongly recommend completing the introductory assignment as preparation for the mandatory ones.

Home assignment Distribution by Canvas on Monday Submission latest on Friday Returned on Friday Re-submission latest on Friday
Assignment 0 Sept 2 (pw 1) Sept 13 (pw 2) Sept 20 (pw 3) Sept 27 (pw 4)
Assignment 1 Sept 23 (pw 4) Oct 4 (pw 5) Oct 11 (pw 6) Oct 18 (pw 7)
Assignment 2 Oct 7 (pw 6) Oct 18 (pw 7) Oct 25 (pw 8) Nov 1 (pw 9)

 

Changes made in the last years

Course name changed from Discrete Event Systems to Logic, Learning, and Decision. The topic on Reinforcement Learning is extended to also include continuous state-space models. The two first home assignments have been merged into one, meaning that the course now only includes 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 perform synthesis and optimization of control functions based on given system models and specifications of desired behavior for the total closed-loop system.
  • Formulate and analyze hybrid systems including discrete and continuous dynamics.
  • Specify temporal logic properties and verify them by 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).

Regular examination date is October 29, am, and first re-sit examination date is January 7, pm. 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:

emanmahmoud838@gmail.com        Eman Mahmoud Ahmed Mahmoud Abosolieb
janina.petereit@gmx.de                      Janina Petereit
kjo02@hotmail.se                                Johan Baumann
lina@vare.se                                         Lina Brink
gongyouli20020512@gmail.com       Youli Gong

To be a study representative means that 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