Course syllabus for Decision-making for autonomous systems

Course syllabus adopted 2023-02-10 by Head of Programme (or corresponding).

Overview

  • Swedish nameBeslutsfattande för autonoma system
  • CodeSSY236
  • Credits7.5 Credits
  • OwnerMPSYS
  • Education cycleSecond-cycle
  • Main field of studyAutomation and Mechatronics Engineering, Electrical Engineering, Engineering Physics
  • DepartmentELECTRICAL ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 35125
  • Open for exchange studentsYes

Credit distribution

0121 Written and oral assignments 3 c
Grading: UG		0 c3 c0 c0 c0 c0 c
0221 Project 4.5 c
Grading: TH		0 c4.5 c0 c0 c0 c0 c

In programmes

Examiner

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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

  1. The MPSYS-course “Discrete event systems”
  2. The MPSYS-course “Modeling and Simulation”
  3. Programming skills (we will use C++)

Aim

The purpose is to introduce the students with Artificial Intelligence methods to enable high-level decision making in robotic systems. The topics of designing a learning system and explainable AI are chosen to enhance the knowledge of students towards the new trends in robotics. This will allow students to have a better understanding of more demanding material to solve real-life problems.

Learning outcomes (after completion of the course the student should be able to)

After completion of the course, the student should be able to:
  • Analyze and apply advanced learning techniques based on logic reasoning. The emphasis will be on learning how to design and deploy learning approaches in different applications such as collaborative robotics.
  • Understand different reasoning methods such as deductive, inductive and probabilistic. Explain their applications and limitations applied to real problems in autonomous systems.
  • Understand the fundamental concepts for designing a learning method to tackle autonomous system problems such as reasoning, learning and prediction.
  • Apply the learned concepts of the explainable Artificial Intelligence methods and assess their performance for complex situations.

Content

The course covers the following topics:

  • Introduction to embodied intelligence, as part of a Cognitive system.
  • Tree-based learning approaches such as Decision trees.
  • Explanation-based learning methods, commonly known as Explainable-AI (XAI). This will include an introduction to knowledge representation and reasoning methods.
  • High-level robot programming. This will include the design and implementation of different modules such as perception, learning, and decision-making for an autonomous system.

Organisation

The course comprises lectures, exercises, and a number of assignments that address important parts of the course. These assignments involve modeling, specification, and programming and are to be handed in.  

Literature

Tom Mitchell: Machine Learning, Mc Graw Hill, 1997 (available online).

Stuart Russell and Peter Norvig: Artificial Intelligence: A Modern Approach, 3rd Ed, Prentice Hall, 2009.

Examination including compulsory elements

Handed in and passed assignments is a necessary requirement to pass the course. The final grade of the course will include the points earned in the assignments and the points earned with the final project.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.