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
Convenant TSH
M. de Haas, MSc
Other course modules lecturer
G.R. Nápoles
Other course modules lecturer
Academic year2020
Starting block
SM 2
Course mode
RemarksCaution: this information is subject to change
Registration openfrom 20/01/2021 up to and including 20/08/2021

After completing the course, the students should have a global picture on the role of knowledge and reasoning both for symbolic and sub-symbolic intelligent systems.

Specific learning objectives to be fulfilled include:

  • Students can explain why knowledge-based and declarative systems are important, how they can be successfully used to solve computational problems and the implications of considering thinking can be seen as a computational process.

  • Students are able to design solutions for model-based reasoning problems having large search spaces by means of heuristic search methods.

  • Students are able to characterize various types of knowledge in line with the degree and type of uncertainty they might pose, the degree of granularity, the suitability to derive explanations that human experts can process and comprehend.

  • Students can identify the fundamental differences between symbolic and sub-symbolic knowledge representation (and reasoning) paradigms. Likewise, students are able to picture how to merge both paradigms into hybrid systems.

  • Students will be able to design and implement logical reasoning systems on the basis of logical techniques in computational languages such as Prolog.

  • Students are able to design solutions in constrained search spaces and manage collections of symbolic data the describe a certain problem domain.

  • Students are able to design knowledge-based neural networks from scratch for modeling complex systems by suing causal reasoning principles. Moreover, students are capable of extracting patterns supporting the decision-making problems.

Students are able to (respectfully) criticize, challenge and elaborate on existing models in order to design improved solutions on the basis of sound arguments.


Knowledge representation, reasoning and learning are key aspects of the Artificial Intelligence field. This course will focus on the first two components, however, we will also briefly address the main ideas and concepts in machine learning.

Knowledge is a pivotal aspect when it comes to designing knowledge-based systems. Actually, it is difficult to imagine an intelligent system operating without any knowledge, either provided by human experts or derived from historical data. In recent years there has been an increasing interest in developing intelligent systems merging those approaches, therefore leading to hybrid intelligent systems. We have reached this point after understanding that neither pure knowledge-based systems nor automatic systems are the silver bullet.

The content of this course can be divided into three major topics: theoretical fundamentals, symbolic knowledge representation and sub-symbolic knowledge representation. These topics are not independent of each other. The theoretical concepts elaborate on general concepts such as artificial intelligence, knowledge, representation, thinking, and reasoning. Many of these concepts inherently have a philosophical connotation. We will also cover aspects related to solutions we intend to find, their taxonomy, heuristic search algorithms that allow exploring a complex solution space in a reasonable amount of time. Towards the end of the first part of the course, we will discuss a procedure to thinking, which is the fancy we (mathematicians, logicians and computer scientists) have given to the logical entailment procedure.

In the second part of the course, we will further dig into the symbolic knowledge representation and reasoning. The Prolog programming language will be our target. This language could be seen as a computational implementation of the logical entailment procedure to manipulate symbolic knowledge structures. The symbolic artificial intelligence in its pure state!

The last part of this course will be devoted to exploring some relevant sub-symbolic knowledge representation and reasoning models. The selected models are based on Fuzzy Cognitive Maps, which are knowledge-based recurrent neural networks to model and simulate complex systems. Here we will go briefly on the concept of machine learning. Furthermore, we will discuss different approaches to reason in presence of uncertainty.

Course available for exchange students
Conditions of admission apply
Contact person
G.R. NĂ¡poles
Timetable information
Knowledge Representation
Required materials
Thinking as Computation by Hector Levesque is an important book to be read. It contains basic concepts concerning symbolic knowledge representation, especially the Prolog programming language. This book guides students through an exploration of the idea that thinking might be understood as a form of computation. Students make the connection between thinking and computing by learning to write computer programs for a variety of tasks that require thought, including solving puzzles, understanding natural language, recognizing objects in visual scenes, planning courses of action, and playing strategic games. The material is presented with minimal technicalities and is accessible to undergraduate students with no specialized knowledge or technical background beyond high school mathematics. Students use Prolog (without having to learn algorithms: “Prolog without tears!”), learning to express what they need as a Prolog program and letting Prolog
Title:Thinking as Computation
Author:Hector Levesque
Publisher:The MIT press
Recommended materials
To be announced
The literature concerning sub-symbolic knowledge representation consists of a collection of academic papers, which have been carefully selected to fulfill the needs of this course. These papers will be provided to students opportunely as a part of the course materials.
Assignment (20%)

Written (80%)

Final Grade

Kies de Nederlandse taal