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Fundamentals of Artificial Intelligence, 7.5 Credits

Swedish name: Artificiell intelligens - grunderna

This syllabus is valid: 2020-08-17 and until further notice

Course code: 5DV124

Credit points: 7.5

Education level: First cycle

Main Field of Study and progress level: Computing Science: First cycle, has at least 60 credits in first-cycle course/s as entry requirements

Grading scale: TH teknisk betygsskala

Responsible department: Department of Computing Science

Established by: Faculty Board of Science and Technology, 2020-09-04


Module 1, theory, 5.5 credits.
The module provides a basic introduction to classical AI (artificial intelligence) as well as non-classical AI. It addresses fundamental conditions, problems and challenges for AI also from a philosophical perspective. Topics covered:

  • Background and history of AI in outline.
  • Fundamental problems and challenges, e.g. - realism, brittleness, scalability, real-time requirements, the frame problem, the homunculus problem, the substrate problem, symbol grounding, common-sense knowledge and common-sense reasoning.
  • Fundamentals of search, e.g. problem, solution, state space, breadth-first, depth-first, heuristics, A*, local search and optimization.
  • Knowledge representation: logic as form of expression (syntax and semantics of propositional logic and first-order logic).
  • Agent paradigms: the hierarchical paradigm, the reactive paradigm, and the hybrid paradigm. Classical planning and execution.
  • Reactive agents, Braitenberg vehicles, subsumption architecture. Potential fields architecture.
  • The physical structure of robots.
  • Teleoperation and semi-autonomous robots.
  • Embodied cognition and situatedness.
  • Neural networks: background and fundamentals.
  • Artificial evolution, genetic algorithms - short introduction.
  • Multiple autonomous agents, swarm intelligence, stigmergy, emergence.
  • Learning - short introduction.

Module 2, laboratory, 2 credits.
In the laboratory module some of the theories, methods and principles treated in the theory part are illustrated and practically applied. This module consists of a number of mandatory laboratory assignments, in part carried out with physical robots or advanced simulators.

Expected learning outcomes

Knowledge and understanding
After having completed the course the student will be able to:

  • give an overview of the field of artificial intelligence, its background, history, fundamental issues, challenges and main directions (ELO 1)
  • interpret and formulate symbolic representations in the form of logical expressions (ELO 2)
  • explain basic concepts, methods and theories for search (ELO 3)
  • account for classical planning of proactive agents (ELO 4)
  • describe methods and theories for reactive agents, architectures based on subsumption, and potential fields (ELO 5)
  • describe the physical structure of robots (ELO 6)
  • account for different degrees of autonomy of robots (ELO 7)
  • explain concepts, methods and theories of embodied cognition and situatedness (ELO 8)
  • explain basic concepts, methods and theories of sensing (ELO 9)
  • explain basic concepts, methods and theories of machine learning (ELO 10)
  • explain basic concepts, methods and theories of artificial evolution, genetic algorithms, multiple autonomous agents and swarm intelligence (ELO 11)

Skills and Abilities
After having completed the course the student will be able to:

  • demonstrate the ability to apply the introduced Artificial Intelligence theories, methods and principles to build basic intelligent software systems. (ELO 12)

Values ​​and approaches
After having completed the course the student will be able to:

  • discuss and analyze social implications of AI technologies in human societies (ELO 13)

Required Knowledge

To be admitted you must have 60 ECTS-credits in Computing Science/Cognitive Science or 2 years of completed studies, in both cases including a basic programming course consisting of at least 7.5 ECTS-credits (e.g. 5DV157, 5DV158, 5DV176, or 5DV177) and either at least 7.5 credits in Data Structures and Algorithms (e.g. 5DV149, or 5DV150) or at least 7.5 credits within Application Programming in Python (e.g. 5DA000) or equivalent.

Proficiency in English equivalent to Swedish upper secondary course English B/6. Where the language of instruction is Swedish, applicants must prove proficiency in Swedish to the level required for basic eligibility for higher studies.

Form of instruction

Education consists of lectures and mandatory computer based assignments. In addition to scheduled activities, individual work with the material is also required.

Examination modes

The assessment of Module 1 (ELO 1-11, 13) is done by a written exam.The grades given in this module are Fail (U), Pass (3) or Pass with Merit (4), and Pass with Distinction (5).

The assessment of Module 2 (ELO 12-13) is done by a number of mandatory assignments. The number of assignments varies depending on the degree of difficulty but is never more than five. The assignments are examined both orally and in written reports. At least one of the mandatory assignments can be done as a seminar in smaller groups. On this module, the grades Fail (U) or Pass (G) are given.

On the course as a whole, the grades given are Fail (U), Pass (3) or Pass with Mark (4), or Pass with Distinction (5). In order to pass the course completely all mandatory modules must be passed as well. The final grade of the course will be the same as the grade of Module 1.

A student who has passed an examination may not be re-examined.

A student who has taken two tests for a course or segment of a course, without passing, has the right to have another examiner appointed, unless there exist special reasons (Higher Education Ordinance Chapter 6, section 22). Requests for new examiners are made to the head of the Department of Computing Science.

Examination based on this syllabus is guaranteed for two years after the first registration on the course. This applies even if the course is closed down and this syllabus ceased to be valid.

Deviations from the examination forms mentioned in this syllabus can be made for a student who has a decision on pedagogical support due to disability. Individual adaptation of the examination forms should be considered based on the student's needs. The examination form is adapted within the framework of the expected learning outcomes of the course syllabus. At the request of the student, the course responsible teacher, in consultation with the examiner, must promptly decide on the adapted examination form. The decision must then be communicated to the student.

Students have the right to be tried on prior education or equivalent knowledge and skills acquired in the profession can be
credited for the same education at Umeå University. Application for credit is submitted to the Student Services / Degree.
For more information on credit transfer available at Umeå University's student web, www.student.umu.se, and the Higher
Education Ordinance (Chapter 6). A refusal of crediting can be appealed (Higher Education chapter 12) to the University
Appeals Board. This applies to the whole as part of the application for credit transfer is rejected.

Other regulations

This course may not be used towards a degree, in whole or in part, together with another course of similar content. If in
doubt, consult the student counselors at the Department of Computing Science and / or the program director of your