Course syllabus

Answer Set Programming, 3 credits

Course code: DT107U Credits: 3
Main field of study: Computer Science Progression: AXX
    Last revised: 04/06/2019
Education cycle: Second cycle Approved by: Head of school
Established: 04/06/2019 Reading list approved: 04/06/2019
Valid from: Autumn semester 2019 Revision: 1

Aims and objectives

General aims for second cycle education

Second-cycle courses and study programmes shall involve the acquisition of specialist knowledge, competence and skills in relation to first-cycle courses and study programmes, and in addition to the requirements for first-cycle courses and study programmes shall

  • further develop the ability of students to integrate and make autonomous use of their knowledge
  • develop the students' ability to deal with complex phenomena, issues and situations, and
  • develop the students' potential for professional activities that demand considerable autonomy, or for research and development work.

(Higher Education Act, Chapter 1, Section 9)

Course objectives

Knowledge and understanding
At the end of the course the student will
• know about the major principles in logic and different standard approaches in logic programming,
• have a better understanding of constraint programming using the stable model or answer set semantics, and
• be able to describe and apply symbolic reasoning methods

Applied knowledge and skills
Completing this course, the student will be able to
• model and formalize different problems such as optimization problems in a declarative way,
• demonstrate their skills in modeling hybrid solutions based on answer set programming (it applies only for those students that are already familiar with non-declarative programming methods), and
• use answer set solvers for modeling and implementing of intelligent systems.

Making judgments and attitudes
Completing this course, the student will be able to
• judge the suitability of symbolic reasoning methods for a given problem, and
• identify problems or misleading results by evaluating the usage of the logical operators, constraints and different types of logical negations used in a given logic program.

Main content of the course

  • Review of First Order Logic.
  • Syntax and semantics of Answer Set Programming.
  • Answer Set Programming (ASP) solving process.
  • Optimization.
  • Stream reasoning and incremental solving process.
  • Hybrid architectures for implementing intelligent systems.

Teaching methods

The course is designed as a distance learning course with a few compulsory activities. It consists of a series of online lectures, compulsory independent study exercises and project assignments.

Examination methods

Exercises, 1.5 credits (Code: A001)
Written reports on task assignments.

Project, 1.5 credits (Code: A002)
Oral presentation at seminar.


For further information, see the university's local examination regulations (in Swedish).

Grades

According to regulations on grading systems for first- and second-cycle education (vice-chancellor's decision 2019-01-15, ORU 2019/00107), one of the following grades is to be used: fail, pass, or pass with distinction. The vice-chancellor or a person appointed by the vice-chancellor may decide on exceptions from this provision for a specific course, if there are special reasons.

Grades used on course are Fail (U) or Pass (G).

Exercises
Grades used are Fail (U) or Pass (G).

Project
Grades used are Fail (U) or Pass (G).

For further information, see the university's local examination regulations (in Swedish).

Comments on grades

Deviations from the U-VG grading scale
Under the Vice-Chancellor's decision RB CF 55-135/2009, deviations from the three-step grading scale (Fail, Pass, Pass with Distinction) are permitted for contract education courses.

Other provisions

The course is given in English.

Reading list and other teaching materials

Gebser, Martin, Kaminski, Roland , Kaufmann, Benjamin and Schaub, Torsten
Answer Set Solving in Practice, Synthesis Lectures on Artificial Intelligence and Machine Learning
Morgan and Claypool, 2012

Baral, Chitta
Knowledge Representation, Reasoning and Declarative Problem Solving
Cambridge University Press, 2003