Abstract

One of the key open questions of artificial intelligence concerns probabilistic logic learning, i.e. the integration of probabilistic reasoning, with first order logic representations and machine learning. The overall goal of the APrIL II project is therefore

to develop a sound theoretical understanding of probabilistic logic learning that enables one to develop effective probabilistic logic learning systems and to apply them on significant real-life applications.

To realize this aim, the APrIL II consortium will

develop a number of significant "show-case" applications of probabilistic logic learning in the area of bio-informatics, more specifically, concerning protein folding, metabolic pathways, and genetics.

develop the needed theory, probabilistic representations, learning algorithms and systems that enables one to learn interesting probabilistic logic models in real-life applications on the basis of data.

The methodology applied is that of the field of inductive logic programming, which explains the title of the project.