Course syllabus for Decision-making for autonomous systems

The course syllabus contains changes
See changes

Course syllabus adopted 2020-02-04 by Head of Programme (or corresponding).

Overview

  • Swedish nameBeslutsfattande för autonoma system
  • CodeSSY235
  • 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 35118
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0109 Written and oral assignments 3 c
Grading: UG		 3 c
0209 Examination 4.5 c
Grading: TH		 4.5 c
  • 16 Jan 2021 pm J
  • 07 Apr 2021 pm J
  • 17 Aug 2021 pm J

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

SSY165 Discrete event systems or similar
ESS101Modeling and Simulation or similar

Aim

The purpose is to introduce the students to concepts of Artificial Intelligence methods to control robots. 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 on 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. The emphasis will be on learning how to design and deploy learning approaches in different applications such as collaborative robotics and autonomous driving. 
    • Understand different probabilistic and hierarchical approaches and their applications 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 & Behavioral trees.
* Probabilistic learning methods, for example, Bayesian methods & Hidden Markov Models.
* Principles behind Reinforcement learning, such as the Monte Carlo method and the Q-learning method.
* Explanation-based learning methods, commonly known as Explainable-AI (XAI). This will include an introduction to knowledge representation and reasoning methods.
* High-level decision making & planning for autonomous systems. Particularly, we will focus on semantic-based and hierarchical learning approaches.
* 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 synthesis and are to be handed in.  

Literature

Course literature will be decided later. 

Examination including compulsory elements

Handed in and passed assignments is a necessary requirement to pass the course. To obtain the grades 4 and 5, satisfactory results in a written examination is in addition required.

The course syllabus contains changes

  • Changes to examination:
    • 2020-09-30: Grade raising No longer grade raising by GRULG