My key interest is Artificial Intelligence grounded in the Logic representation of problems. In particular, Statistical Relational Learning to address noise and incomplete knowledge.
In my current research project, Zugzwang | Logic and Artificial Intelligence, we try to address a major limitation of logical representations, the implicit assumption that the Background Knowledge (BK) is perfect. This assumption is problematic if data is noisy, which is often the case. Here we aim to explore how ASP specifications with probabilistic facts can lead to characterizations of probability functions on the specification's domain.
I am integrated member of NOVALINCS, a leading research lab in the area of Computer Science and Informatics, hosted at the Departamento de Informática – NOVA School of Science and Technology | FCT NOVA – Universidade NOVA de Lisboa, a pioneering national institution in the field. NOVA LINCS also integrates branches at Universidade de Évora, Universidade da Beira Interior and Universidade da Madeira. NOVA LINCS research team currently engages 61 integrated members and 23 associated members.
I've done some work on the simulation of physical systems, but this (strange) video is the only real result.
I've drafted a few cheat-sheets (now not working properly) in Portuguese about
XSLT sheets to convert OpenMarkov's ProbModelX (pgmx) ↔ XMLBIF (xml).
