Established by: Faculty Board of Science and Technology, 2024-03-14
Contents
The course is divided into two modules:
Theory and methodology 4.5 hp
Practical application of theory and methods in a project 3.0 hp
The theoretical module covers theories and methods in Artificial Intelligence (AI) and Human-AI Interaction for designing and constructing intelligent interactive systems and environments. Parts of the module are applied in the practical module, which consists mainly of a project that is conducted individually or in group.
Emergent trends in AI aim to combine different fundamental theories and methods within the AI field and across disciplines such as cognitive science and social science to solve societal challenges, and to build new more advanced AI. This course focuses on such trends in interactive intelligent environments that include people and AI-based systems, and where intelligent systems and people pursuing different goals are expected to collaborate. This advanced level course takes a research perspective on theories and methods for interactive intelligent systems, which is done from the perspective of solving societal challenges.
The student will deepen her/his knowledge about state-of-the-art in AI topics such as knowledge representation and reasoning, machine learning, multiagent systems and agent societies, and gain knowledge in how these methods and theories can be combined to achieve AI useful for human-AI collaboration.
The student is encouraged to engage with a research group during the course, and target particular challenging and unsolved issues relating to interactive intelligent systems.
Expected learning outcomes
Knowledge and understanding After having completed the course the student should be able to:
(FSR 1) Design and explain architectures for intelligent controllers (rational agents) that are specific for interactive intelligent systems, and that include complementary AI technologies.
(FSR 2) Design the interactive intelligent system for human-AI collaboration, and explain how adaptation and personalization are accomplished.
(FSR 3) Show deepened knowledge and understanding of the possibilities and limitations of existing AI technologies.
Skills and abilities After having completed the course the student should be able to:
(FSR 4) Read and explain scientific articles in the field of interactive intelligent environments.
(FSR 5) Demonstrate practical skills in developing and evaluating interactive intelligent environments/systems that include intelligent controllers and that collaborate with humans.
Values and attitudes After having completed the course the student should be able to:
(FSR 6) Evaluate theoretical and practical results critically and compare these with theoretical, technological and societal (e.g., ethical, safety, security, etc) expectations.
Required Knowledge
At least 90 ECTS, including 60 ECTS Computing Science, or 120 ECTS within a study programme. At least 15 ECTS programming; 7.5 ECTS logic; the course "5DV243 Artificial intelligence" (or equivalent); and the course "5DV181 Artificial intelligence - methods and applications" (or equivalent). Proficiency in English equivalent to the level required for basic eligibility for higher studies.
Form of instruction
The course consists of lectures, project work in computer labs and other environments, and exercises in small groups. In addition to scheduled activities the course also requires individual work with the material.
Examination modes
Examination of the theoretical module (FSR 1-3) is done through a written examination in halls. The grade on this module is one of the grades Fail (U), Pass (G) or Pass with Distinction (VG).
The examination of the practical module (FSR 3-6) is done through completing a project in-group or individually according to instructions provided during the course. Parts of the project work may be done through field studies outside the university in collaboration with societal organisations, and meetings during the project can be located at such organisation. The grade on the practical module is one of the grades Fail (U) or Pass (G).
A student that has failed the practical module of the course but has regularly attended a majority of the project activities can be given a re-exam covering the parts that the student has missed. If a student has not participated in the project activities (or missed a majority of them), the student can enroll in the practical part next time the course is given. The student does not have the right to continue with the same project the next time (s)he attend the course, and may need to start over with the project work in collaboration with a new student group and with a new topic.
On the whole course one of the grades Fail (U), Pass (G) or Pass with Distinction (VG) is given. At least the grade Pass must be achieved on each part in order to get a grade for the whole course. The grade given on the course is the same as the one set on module 1.
Adapted examination The examiner can decide to deviate from the specified forms of examination. Individual adaptation of the examination shall be considered based on the needs of the student. The examination is adapted within the constraints of the expected learning outcomes. A student that needs adapted examination shall no later than 10 days before the examination request adaptation from the Department of Computing Science. The examiner makes a decision of adapted examination and the student is notified.
Other regulations
If the syllabus has expired or the course has been discontinued, a student who at some point registered for the course is guaranteed at least three examinations (including the regular examination) according to this syllabus for a maximum period of two years from the syllabus expiring or the course being discontinued.
Literature
Valid from:
2025 week 4
Articles and material provided by the department or available online.
