Course syllabus
Course-PM
SSY236 SSY236 Decision-making for autonomous systems lp2 HT25 (7.5 hp)
This course is offered by the Department of Electrical Engineering
Aim
The purpose is to introduce the students to 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.
Prerequisites
SSY165 Discrete event systems or similar
ESS101 Modeling and simulation or similar
Programming skills (we will use C++)
Examiner and Lecturer
Karinne Ramirez-Amaro, karinne@chalmers.se
Supporting lecturer
Alex Mitrevski, alemitr@chalmers.se
Teaching assistants
Jing Zhang, jingzhan@chalmers.se
Zhitao Liang, zhitao@chalmers.se
Student Representatives
MPSYS n.andersson01@hotmail.com Noel Andersson
MPSYS fredlundebba@gmail.com Ebba Fredlund
MPSYS emma.e.hedberg@gmail.com Emma Hedberg
MPSYS kim.hotvedt1@outlook.com Kim Hotvedt
MPCAS eliaswilsborn12@gmail.com Elias Wilsborn
Course purpose
This 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. Most of the implementation will be done in C++, therefore basic knowledge is required.
Learning objectives and syllabus
- Analyze and apply advanced learning techniques based on logical 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 AI methods and assess their performance for complex situations.
Link to the syllabus on Studieportalen.
Schedule
Note: The information in TimeEdit is not updated. Here is the correct schedule, which includes the information on the rooms:
Course literature
The following list provides suggested literature for this course. This literature is not mandatory, but is intended to provide additional support for the course. Participants may use it to acquire more detailed information about the topics covered in this course.
- T. M. Mitchell, "Machine Learning", McGraw-Hill.
- S. Russell and P. Norvig, "Artificial Intelligence: A Modern Approach", Pearson.
Additional material will be provided via Canvas for the course.
Communication
Canvas will be used as our main communication means between students and teachers.
Students are also encouraged to use the discussion forums to interact with other students. Asking in the forum particularly allows other students to chip in and benefit from the provided answers.
In the rare event that you have a private question that doesn't belong in the course forum, you can contact us directly via email. For course-related emails, please prefix the email subject with the designator [SSY236].
Changes made since the last occasion
No changes have been made since the last occasion.
Examination form
The final grade of this course will be awarded via continuous assessment in the form of 1) team assignments, 2) online quizzes, and 3) a final project
Total grade = Assignment + Quizzes + Final project
The table below will be used to convert the number of points to the final course grade:
| Number of points | Final grade |
| 87 - 100 | 5 |
| 73 - 86.9 | 4 |
| 60 - 72.9 | 3 |
| 0 - 59.9 | fail |
Assignments
The goal of each assignment is to provide practical experience in implementations of the learned AI & reasoning algorithms applied in a robotic system.
To pass the laboratory of this course, the students are required to pass four team assignments. An assignment will be considered a "pass" when it completes at least half of the assigned points. Each assignment will have a set of tasks, which in total can accumulate different points as follows:
| Assignment | Number of points | Min points to pass the assignment |
| A01 | 8 | 4 |
| A02 | 10 | 5 |
| A03 | 12 | 6 |
| A04 | 18 | 9 |
- Each assignment will be considered a "pass" when it completes at least half of the assignment points (see above table). The assignments should be strictly delivered on time according to the course schedule.
- The maximum number of students in a team for the assignments is two!
- Any assignment submitted after the deadline will be considered as not delivered (it will be marked as failed and you will not be able to re-submit it). This means that you will fail the laboratory of this course.
For each assignment, you need to deliver original material in the form of code or a simulation file, depending on the assignment. The code must be accompanied by a short report, e.g. a README file that describes the delivered solution and how to run it, especially in the case the delivered material requires a custom initialization. All submissions will be expected via Canvas.
Note: Some of the assignments have extra points. There are at least six extra points that can only be used to increase your grade but not to pass the assignments.
Online quizzes
In addition, the active participation of the students in the lectures and tutorial sessions will be evaluated via online (Canvas) quizzes. The quizzes should be done individually and you can earn 0-20 points. We encourage the students to answer all the online quizzes.
Final project
To improve your final grade, you can submit your solutions for the final project which combines all the main topics covered in the lectures as well as the assignments, quizzes, and tutorials of this course. The final project will be performed in teams of two people.
For the final project, each member of the team can earn between 0-32 points. Each team will present their obtained results for the final project, where each member of the team needs to present his/her contributions in a meeting with the supervisors. Each team will have a final project meeting to demonstrate their obtained results.
Note: Even when you do the assignments and project work in teams, you will be examined individually. Every project member should be involved in all parts of the project. Failing to actively contribute to all parts of the project might result in a failed grade.
Note on originality
Each assignment and the final project must deliver original material:
• In the case of detected "copied" material (i.e. if you do plagiarism), all the copies will be rejected (marked as not delivered). Therefore, you cannot resubmit the copied assignment and you will fail the laboratory part of this course.
• If the material is based on external sources (e.g.. code from the internet, ChatGPT or similar), the original source must be properly acknowledged in the comments of the delivered code, indicating the URL and the main differences.
• A brief report (e.g. README file) should be delivered with each assignment. This report should contain a brief solution description including how-to instructions.
Individual responsibilities
Every member of a group (e.g. assignment team) should take full part in every aspect of the assignment or project and be ready to answer any question. It is every group member's responsibility to make sure that every group member fully participates in the project. If a group has a member who does not contribute and follow the project, the group members should contact the teacher responsible for the group. Freeriders are not accepted and risk failing the project. Individual group evaluations will be carried out regularly. Members of the same group might get a different number of points.
Academic work relies on respect for other people's knowledge and ideas. By representing someone else's work or ideas as your own you commit a serious offence and violate Chalmers rules. By violating the conditions stipulated in the document "Rules of Discipline" it is possible to be suspended or expelled from Chalmers. More information can be found in the documents below.
Course summary:
| Date | Details | Due |
|---|---|---|