The course aims to develop the doctoral student in the field of knowledge representation in machines, including practices, history and philosophy.
The course includes:
- Seminar discussion of the required reading;
- Experiment with a modified version of the Turing test and including interaction between intelligent agents in a game-theoretical context. The student will also explore these concepts by conducting similar exercises with undergraduate students;
- Empirical studies on the topic: "How intelligent are modern IT systems/services?"; How to present/communicate information/knowledge to users? Surveys are conducted preferably in teaching environments.
Intended course learning outcomes
To obtain a passing grade, the doctoral student shall demonstrate:
- Fundamental knowledge of the history, philosophy, and techniques pertaining to knowledge representation in machines.
- Understanding the similarities and differences between communication and knowledge transfer between machine-machine, machine-human and human-human.
- Ability to apply these ideas in complex situations where more than two parties are involved, e.g. computer-supported collaboration in teams.
- Ability to apply these concepts in an educational context.