Unifying Frameworks for Non-Monotonic Reasoning
ID
MSCA-2020-BBogaerts03
Supervisors
Project description
I am interested in research on unifying frameworks in the domain of knowledge representation and reasoning. Of particular interest are: approximation fixpoint theory and justification theory (and especially: investigating the relation between these two).
Approximation Fixpoint Theory (AFT) is a lattice-theoretic framework that was founded in the early 2000s by Denecker, Marek, and Truszczy\'nski as a way of unifying the semantics of different non-monotonic logics. In recent years, interest in AFT has gradually increased, with applications now ranging from foundations of database theory to abstract argumentation. Motivated by the success of AFT in this wide range of applications, I am interested in extending, as well as further analyzing this framework.
Based on an old characterization of semantics of logic programs by means of justifications Denecker et al recently introduced a general, abstract \textbf{theory of justifications}. Justification theory can characterize semantics of many knowledge representation formalisms and is complementary to AFT. Like approximation fixpoint theory, this theory can also characterize semantics of many other knowledge representation formalisms. Unlike AFT, justification theory does not work for arbitrary lattice operators, but is focused on powerset lattices. On the other hand, justification theory provides a notion of nesting and refined information such as \emph{why} a certain atom holds. There seem to be strong correspondences between the two theories, including similar notions of duality. However, the exact relationship between the two domains is largely unexplored.
About the research Group
Artificial Intelligence Lab
The Artificial Intelligence Lab, founded in 1983 by Prof. Dr. Luc Steels, is the first AI lab on the European mainland. It is headed by Prof. Dr. Ann Nowé and Prof. Dr. Bernard Manderick. During its history of more than three decades, the VUB AI Lab has been following two main routes towards the understanding of intelligence: both the symbolic route (classical AI) as the dynamics route (complex systems science).
Since its foundation, more than 50 people have received a PhD degree at the Artificial Intelligence Lab, of which 18 in the past 5 years only. The total amount of publications sums up to more than 850 publications which are cited more than 24.000 times, 8500 times in the last 5 years. The lab also has experience in setting up of spin-offs (5 since its beginning). The research group is provided with standard hardware and software utilities and departmental services (secretary and ICT support). The COMO group of the AI-lab focuses on the one hand on the modeling of natural phenomena, and on the other hand on developing algorithms for complex problem solving inspired by these natural phenomena. The lab has experience in a wide range of learning techniques such as: Reinforcement Learning, Genetic Algorithms, Neural Networks, Support Vector Machines, Graphical models including Bayesian Networks, Genetic algorithms etc.