At the University of Luxembourg.
Defended on November 9, 2020.
At the University of Luxembourg.
Specializations validated:
At Imperial College London.
Topics studied: Artificial Intelligence, software design, machine learning, robotics, ...
Thesis:
Designing a learning tool for argumentation theory.
Discrete structures are foundational material for computer science. Relatively few computer scientists will be working primarily on discrete structures, but many other areas of computer science require the ability to work with concepts from discrete structures. The discrete structures covered in this introduction include important material from such areas as set theory, logic, graph theory, and number theory.
The objective of this course is to introduce students to knowledge representation and reasoning methods for intelligent agent systems. The course has 4 parts:
The objective of this course is to prepare the student for individual research work (e.g. a master/PhD thesis) in Artificial Intelligence, Knowledge Representation, and Reasoning by reading research literature on a specific topic and realizing a small project. It offers a gentle introduction into current research issues linked to the modeling of intelligent agents, paying special attention to those addressed in the Interdisciplinary Lab for Intelligent and Adaptive Systems (ILIAS). The topics can vary. In the past years, we have discussed in particular causal reasoning, belief dynamics, defeasible reasoning, real-world argumentation, story modeling, generalized reasoning under uncertainty, epistemic decisions, and reasoning about actions. This year's seminar theme will be "Modeling reasoning in scientific texts". We will discuss research literature concerned with this topic and investigate how the proposed theoretical tools can be applied to analyze reasoning in selected scientific texts. Basic knowledge in logic and knowledge representation is required. If necessary, additional background will be provided.
3rd floor, Maison du Nombre
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