Course syllabus
Course-PM
SSY236 SSY236 Decision-making for autonomous systems lp2 HT24 (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
Teaching Assistants
Maximilian Diehl, diehlm@chalmers.se
Jing Zhang, jingzhan@chalmers.se
Student Representatives:
MPSYS tovecasparsson@gmail.com Tove Casparsson
UTBYTE Adrien_Jacquet-Cretides@etu.u-bourgogne.fr Adrien Jacquet Cretides
MPMOB fereshtehnaderi6@gmail.com Ariel Naderi
MPSYS schyumoscar@gmail.com Oscar Schyum
MPSYS wd13855655234@163.com Dian Wang
Read more about your role as a student representative, here:
https://www.chalmers.se/en/education/your-studies/plan-and-conduct-your-studies/course-evaluation/
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.
Schedule
Course literature
The following list provides suggested literature for this course. This literature is not mandatory. It 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.
Machine Learning. T. M. Mitchell, McGraw-Hill.
Artificial Intelligence: A Modern Approach, S. Jonathan Russell, P. Norvig, 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. Note, that students are encouraged to also use the discussion forums to interact with other students.
Changes made since the last occasion
- The home assignment structure changed as follows:
- The number of assignments has increased to four. This year, the students can solve the assignments in teams of two people. Therefore, the content of the assignments has changed with respect to last year.
- All the tutorials and assignment descriptions will be carried out at the PSL computer lab where the computers have the Operating System: Ubuntu 20.04.
- We removed the "Mandatory attendance" for all the tutorials. There is no requirement for attendance to pass the course, however we highly encourage your participation.
- To pass the laboratory, the students need to pass all four assignments.
- The students have the opportunity to resubmit a maximum of two failed assignments before January 9, 2025. The only condition is that failed assignment(s) have received at least 1 point at the original submission. Assignments that were not originally submitted within the deadline cannot be re-submitted.
- There are at least six bonus points in some assignments!
- This year there are NO minimum points requirement to pass the final project. The course can be passed by earning enough points from assignments and quizzes alone, without the need to complete the final project.
- Four new tutorials are included this year to gather more understanding to fulfil the assignments and final project.
- [Optional] We will provide installation instructions to use a system based on Dockers to work on the assignments using your own laptops (if they have enough resources). We, however, strongly encourage you to use the PSL computer labs for the assignments.
- The exercise session has been revised and they will be better connected to the lecture topics.
Learning objectives and syllabus
- 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.
Link to the syllabus on Studieportalen.
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.
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
The goal of each assignment is to provide practical experience in implementations of the learned AI & reasoning algorithms applied in a robotic system. Each assignment will have a set of tasks, which in total can accumulate different points as follows:
| Assignments | Max 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.
In total, you will have to deliver four assignments, and then the laboratory will be passed if the student passes all assignments.
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. 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.
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.
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.
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.
Finally, your final grade is computed with the following formula:
Total grade= Assignment + Quizzes + Final project
See the below table 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 |
NOTE: This year, 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.
Important note about assignments and final project: Each assignment must deliver original material, similarly for the final project:
• In the case of detected “copied” material, 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 (i.e. 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 a 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 |
|---|---|---|