Kies de Nederlandse taal
Course module: 800804-B-6
Knowledge Representation
Course info
Course module800804-B-6
Credits (ECTS)6
CategoryBA (Bachelor)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Humanities and Digital Sciences; TSH: Department Cognitive Science and AI; TSH: Department Cognitive Science and AI;
Is part of
B Communication and Information Sciences
B Communication and Information Sciences (Spc.Cog.Sc & AI)
B Cognitive Science and Artificial Intelligence
PM Data Science and Society
Contact personM. van Otterlo
Coordinator course
M. van Otterlo
Other course modules lecturer
M. van Otterlo
Other course modules lecturer
dr. M. Postma
Other course modules lecturer
Academic year2018
Starting block
SM 2
Course mode
RemarksThis information is not up to date. Check the Course Catalog 2019 or select the course via “Register”.
Registration openfrom 14/01/2019 up to and including 31/07/2019

The goal of the course is to provide an introduction into knowledge representation and reasoning mechanisms in artificial intelligence. This requires making use of techniques from the field of logic, of logic programming and Bayesian probability.

After completing the course

  • Students can explain why knowledge-based and declarative systems in AI and cognitive systems are important, how they can be used to solve computational problems, and why and how thinking can be seen as a computational process.

  • Students are able to distinguish various problem solving methods, types of model-based reasoning, and planning.

  • Students are able to characterize various types of knowledge representation for aspects such as sequence/time, action, ontology and uncertainty, and they will be able to use them to model and solve problems.

  • Students will be able to distinguish different kinds of applications where knowledge representation can be used effectively, and can evaluate which types of formalisms satisfy the requirements in those applications.

  • Students will be able to employ propositional and relational representations for reasoning about deductive, constraint-based and abductive (explanation-focused) knowledge questions.

  • Students will be able to design and implement small, but serious, logically reasoning systems on the basis of logical (and probabilistic) techniques in computational languages such as Prolog and the AI-dialect AILog.

  • Students have obtained knowledge of a selection of additional topics in knowledge representation such as description logics, temporal formalisms, spatial logic, answer set programming, or inductive logic programming (this part can vary depending on the group of students).


Knowledge representation, reasoning and learning can be seen as the three most prominent aspects of artificial intelligence; this course will focus mainly on the first two elements. Knowledge representation plays a central role in knowledge-based systems, but also in intelligent systems in a more broad sense. In recent years there is an increased interest in model-based techniques, ranging from model-based diagnosis of illnesses in medical systems, to fault and maintenance prediction in systems and machines, to the use of prior knowledge in visual scene understanding, and to general developments in the semantic web. Knowledge representation methods allow for explicit representation of knowledge about domains (e.g. time, dynamics, ontologies, databases with facts, and commonsense knowledge) and, in addition, allow for explicit reasoning about that knowledge to infer conclusions. Furthermore, the modeling of actions and dynamic situations in such formalisms will be covered. Students will obtain knowledge about some formalisms, and they will practice modeling and reasoning styles through exercises and/or small programming assignments. In addition, students will learn logic programming and complete several practical assignments which involve modeling and programming. Reasoning with uncertainty has obtained a dominant position too in artificial intelligence and will therefore be covered (briefly) at the end of this course, including the recent trend to combine logical and probabilistic reasoning in so-called probabilistic logic programming languages.


The course is evaluated on two different aspects:
1) The (individual) exam counts for 60 percent, and is scored between 0 and 100.
2) Take-home modeling and programming assignments (usually done by pairs of students) which combined count for 40 percent and are scored between 0 and 100 individually.
The final grade (between 0 and 10) for the course is computed using the weighted average of the components.

Students are required to hand in small exercises after most of the lectures, to encourage keeping up with the lectures, and finishing them is a requirement for passing the course.

Important note for the 2018-2019 edition
This course is new, and immediately starting next year it will be offered as a first-year course, which also is reflected in the current year's setup, content and level.

Timetable information
800804-B-6|Knowledge Representation
Written test opportunities
Written test opportunities (HIST)
Schriftelijk (60%) / Written (60%)EXAM_01SM 2114-06-2019
Schriftelijk (60%) / Written (60%)EXAM_01SM 2205-07-2019
Required materials
This is the main book of the course (next to other literature)
Title:Thinking as Computation (paperback)
Author:Hector J. Levesque
Publisher:The MIT Press
This is the second book in the course, from which we will cover a few chapters. You do NOT have to buy this book, since it is available online at (of course there is a physical book which you could buy, if you want)
Title:Artificial Intelligence: Foundations of Computational Agents
Author:David L. Poole and Alan. K. Mackworth
Publisher:Cambridge University Press
Recommended materials
To be announced
All other materials will be, in due time, made available on the Blackboard page of this course.
Assignment (40%)

Written (60%)

Final Grade

Kies de Nederlandse taal