Machine Learning and Natural Language Processing Lab

Seminar/Practical Course

Probabilistic Logic Learning

Prof. Dr. Luc de Raedt

Co-Organizer: Kristian Kersting

• Times:
• Wednesday 14-16 o'clock (SR -1- 019 Geb. 079)

• Credit Points (Kreditpunkte):
• 3/3

• Language:
• English

• Overview:

The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the intersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of different formalisms and learning techniques have been developed by many researchers from a variety of backgrounds (including machine learning, statistics, inductive logic programming, databases, and reasoning under uncertainty). The aim of this research line diverges from traditional approaches in these fields that assume data instances are structurally identical and statistically independent or assume that relationships are deterministic. Several workshops (SRL-00, MI-19, SRL-03), reasearch projects ( EELD, APrIL I, APrIL II, ...), and (invited/honorary) talks (such as D. Koller at IJCAI-01, ICML-03/KDD-03 and F. Provost at ICML-03) have been devoted to probabilistic logic learning. The courses will explore approaches to probabilistic logic learning. We will explore the foundations (i.e. knowledge representation, reasoning and learning within both traditional and upgraded probabilistic frameworks), tasks and applications (e.g. collaborative classification and filtering, link discouvery, bioinformatics, ...), and advantages, and limitations of the surprising array of approaches that have been developed over the past decade. More precisely, we will mainly focus on probabilistic-logical models (PLMs). PLMs integrate probability theory with some first order logic. Traditionally, a probabilistic formalism like Bayesian networks or hidden Markov models is selected and upgraded by incorporating some logic such as entity-relationship (ER) models, Datalog, or Prolog. Frameworks developed include probabilistic relational models (PRMs), stochastic logic programs (SLPs), Bayesian logic programs (BLPs), relational Bayesian networks (RBNs), relational probability trees, first-order Bayesian classifiers, relational Markov models, block models, statistical relational (SRMs) models and relational reinforcement learning (RRL).

• Introductory Meeting (Vorbesprechung):
• Wednesday 26, October

• Consultation hours:
• Please send an email to Kristian Kersting