Course syllabus
Course-PM
SSY235 Decision-making for autonomous systems lp2 HT20 (7.5 hp)
This course is offered by the Department of Electrical Engineering
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 of more demanding material to solve real-life problems.
Prerequisites
SSY165 Discrete event systems or similar
ESS101Modeling and Simulation or similar
Programming skills (we will use C++)
Examiner
Karinne Ramirez-Amaro, karinne@chalmers.se
Lectures
Karinne Ramirez-Amaro, karinne@chalmers.se
Emmanuel Dean, deane@chalmers.se
Teaching Assistants
Maximilian Diehl, diehlm@chalmers.se
Student Representatives:
Marcus Anjemark, anjemark@student.chalmers.se
Marcus Höglander, marhogl@student.chalmers.se
Jiraporn Sophonpattanakit, sophon.jira@gmail.com
Judith Treffler, judith-treffler@t-online.de
Read more about your role as student representatives, here: https://student.portal.chalmers.se/sv/chalmersstudier/minkursinformation/kursvardering/Sidor/Att-vara-studentrepresentant.aspx
Schedule
A detailed schedule of all the course activities is presented in the following link:
Note that the team meetings about the project are compulsory as well as the final project presentations.
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
Zoom will be used for meetings between students and teachers. Note, that students within a project group can also set up meetings in Zoom to interact with each other.
Examination
The final grade of the course will include the points earned in the assignments and the points earned with the final project (i.e. in total you can accumulate 100 points). To pass this course you require to pass the assignments and the final project.
Assignments: You can earn 0-30 of the total points. To pass the laboratory assignments, you need to pass at least three assignments, where two of them should be individual assignments and at least one team assignment (see below for more details).
Final project: You can earn 0-70 of the total points. A passing grade for the project requires at least 40 points.
Total grade= Assignments + Final project
= 30 points + 70 points = 100 points
| Number of points | Final grade |
| 85 - 100 | 5 |
| 68 - 84.9 | 4 |
| 52 - 67.9 | 3 |
| 0 - 51.9 | fail |
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Compulsory laboratory Assignments (5 Assignments): The goal of these assignments is to provide practical experience on implementations of the learned AI algorithms applied in a robotic system. Each assignment will have a set of tasks, which in total can accumulate 6 points (per assignment). Each assignment will be considered as "pass" when it completes at least 4 out of those 6 points. The assignments should be strictly delivered on time according to the course schedule. Any assignment submitted after the deadline will be considered as not delivered (it will be marked as fail). In total you will have 5 assignments, then the laboratory will be passed when the participant delivers at least 3 correct (passed) assignments, with the following conditions:
- You need to pass at least two individual assignments. The first three assignments will be delivered individually.
- You need to pass at least one team assignment. The last two assignments will be delivered in teams.
For all the assignments (individual and teams), 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 that describes the delivered solution and how to run it (in the case the delivered material requires custom initialization).
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Compulsory final project. An important part of the course is a final project that combines all the main topics and assignments covered in this course. The final project should include the topics reasoning+learning+robotics. The project is solved in teams of two-four students, preferably, the team members need to have different backgrounds. Even when you do the project work in teams, you will be examined individually. Every project member should be involved in all parts of the project and the essay. Failing to actively contributing to all parts of the project might result in a failed grade. Each team will present the obtained results for the final project, where each member of the team needs to present his/her contributions. Additionally, a final project report will be also delivered. During the examination week, the schedule for the final project presentations is defined in the course schedule. You can earn 0-70 points on the project. A passed project requires at least 40 points. The evaluation criteria for the final project includes:
- The complexity of the project
- The final presentation of the project
- The final report/essay of the project
- The code and documentation of the final implementation of the project
- Group evaluation report
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).
• If the material is based on external sources (i.e. code from the internet!), the original source must be properly acknowledged in the comments of the delivered code, indicating the URL and the main differences.
• A brief report should be delivered with each assignment (brief solution description and mini-howTo instructions).
Individual Responsibilities
Every member of a project group should take a full part in every aspect of the project and be ready to answer any question. It is every group members responsibility to make sure that every group member fully participates in the project. In case a group has a member that 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 evaluation at the end of the project. Members of the same group might get a different number of project 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.
Every group has two project meetings. Attendance is mandatory. All students should be present in the final project presentation. In case you get sick you have to contact the examiner.
Learning objectives and syllabus
Learning objectives:
- 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.
Link to the syllabus on Studieportalen.
If the course is a joint course (Chalmers and Göteborgs Universitet) you should link to both syllabus (Chalmers and Göteborgs Universitet).
Course summary:
| Date | Details | Due |
|---|---|---|