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Application of Probabilistic Inductive Logic Programming

Objective

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 (1) 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.(2) develop the needed theory, probabilistic representations, learning algorithms and systems for learning 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.

Field of science

  • /natural sciences/computer and information sciences/artificial intelligence
  • /natural sciences/biological sciences/biochemistry/biomolecules/proteins/protein folding

Funding Scheme

STREP - Specific Targeted Research Project

Coordinator

ALBERT-LUDWIGS-UNIVERSITAET FREIBURG
Address
Georges-koehler-allee, Building 079
79110 Freiburg
Germany

Participants (4)

IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
United Kingdom
Address
180 Queen's Gate
SW7 2BZ London
INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE
France
Address
Domaine De Voluceau
78153 Le Chesnay
UNIVERSITA DEGLI STUDI DI FIRENZE
Italy
Address
Via Di Santa Marta 3
50139 Firenze
UNIVERSITY OF HELSINKI
Finland
Address
Teollisuuskatu 23
00014 Helsinki