Track title: 
Students can choose from three tracks:

Track description: 

Track title: 
Agents

Track description: 

An intelligent agent is an artificial (computer-based) entity that can act proactively, reactively, autonomously, and rationally in a dynamic environment. In this track, you will focus on the logical modelling, programming, and application of intelligent agents and multi-agent systems. You will also explore the use of probabilistic and sub-symbolic machine learning techniques to make agents learn and adapt to their environments as well as to other agents.

Compulsory courses

Intelligent Agents 
Intelligent Agents addresses the theory and realisation of so-called intelligent agents, pieces of software that display some degree of autonomy, realised by incorporating 'high-level cognitive / mental attitudes' in their design. The course treats philosophical and logical foundations and agent programming languages, focusing on knowledge representation, reasoning and planning.

Multi-Agent Learning 
Multi-agent learning (MAL) studies techniques for making software agents learn from and adapt to the behaviour of other software agents. Examples of challenging multi-agent domains that need robust MAL algorithms are manufacturing systems, distributed sensor networks, robo-soccer, disaster rescue and recreational games of imperfect information such as poker. 

Multi-Agent Systems
This course is about computer systems consisting of several interacting intelligent agents. Topics addressed are game theory, auctions, communication, social choice, mechanism design and normative multi-agent systems.

Track title: 
Cognitive Processing

Track description: 

The cognitive processing track focuses on how AI techniques can help to understand human behavior and cognition. Specifically, you learn how human behavior can be captured in computer simulations (cognitive models) and how the predictions of these simulations can be tested in experiments. In the track you gain theoretical knowledge about and hands-on experience with modeling and experimentation.

Compulsory courses

Cognitive Modeling
Formal models of human behavior and cognition that are implemented as computer simulations - cognitive models - play a crucial role in science and industry. In science, cognitive models formalize psychological theories. In industry, cognitive models predict human behavior in intelligent 'user models', that adapt situations (e.g., games, interfaces, training) to individual users. In this course you learn how to develop, implement, and evaluate various types of cognitive models.

Experimentation in Psychology and Linguistics
In science and industry, behavioral experiments are used frequently. Scientists use experiments to test their theories (e.g., that come from models) of human behavior. Industry uses experiments to test products and how people use them. In this course you will learn how to design, implement, and analyze good experiments.

Advanced Topics in Cognitive Science
To be a good scientist and practitioner in the field of Cognitive Science and Artificial Intelligence, you need to be able to evaluate recent research in the field. In this seminar you learn about recent findings from our researchers. Moreover, you will discuss this research with the PIs, critique their work, and develop your own research proposals that build on it.

Track title: 
Reasoning

Track description: 

The ability to reason is one of the primary forms of intelligence. Since language and reasoning are closely intertwined, the study of language is an important component in the study of reasoning. In this track, you will address the questions of what correct reasoning is, how people can rationally reason with incomplete and uncertain information and resolve conflicts of opinion, and how reasoning and language interact.

Compulsory courses

Logic and Language
This course covers advanced methods and ideas in the logical analysis of language, especially in relation to type-logical grammars, the parsing-as-deduction paradigm, and their combination with formal semantics of natural language.

Commonsense Reasoning and Argumentation
This course makes the students familiar with logics for reasoning with incomplete and uncertain information, and with logical frameworks for reasoning and interacting by way of constructing arguments and counterarguments, to resolve conflicts of opinion.

Logic and Computation 
This course covers a selection of advanced topics in computational logic in the areas of computational tractability, complexity of reasoning systems, type theory and proof checking.