Home Organizers Goals Content StarAI

Tutorial on StarAI

Statistical Relational AI at KI-2018

The 41st German Conference on Artificial Intelligence
September 24-28th, 2018, Berlin, Germany

Organizers

The tuturial is organized by Tanya Braun, Kristian Kersting, and Ralf Möller

Tanya Braun is a research associate at Universität zu Lübeck,
Kristian Kersting is a professor for Machine Learning at Darmstadt University, and
Ralf Möller is a professor for Information Systems at Universität zu Lübeck

Goals

The goal of the tutorial is to give an overview about recent developments in probabilistic relational modeling and reasoning

Probabilistic relational modeling for artificial intelligence (also called statistical relational AI) has attracted the interest of a huge number of researchers in the last ten years. Assuming a background in probability theory as well as in basics about Bayesian or Markovian networks, we introduce specific probabilistic relational representation languages, exact inference algorithms, and learning techniques. Using a tele-healthcare application example we demonstrate achievements and challenges.

Contents

We cover StarAI from the foundations to applications

  1. Introduction (Kristian)
    • Probabilistic relational models (PRMs)
    • StarAI
    • Systems AI
  2. Probabilistic relational models: languages and applications (Ralf)
    • Landscape overview, semantics (grounded-distributional, maximum entropy principle)
    • Inference problems (margin queries with and without evidence, filtering, smoothing, most-probable explanation, maximum a posterior)
    • Lifted inference for grounded semantics: central ideas
    • Learning parameters and structure of PRMs, basics: maximum likelihood, expectation maximization, first-order inductive learning: where PRM learning started
    • Reusing ontologies for PRMs, computing ontologies for the job from PRMs
    • Actions and utilties, simple decisions: maximum expected utility, sequential decisions: partially observable first-order Markov decision problems
  3. Specific topics on inference and learning
    • Exact lifted inference (Tanya)
    • Exact lifted dynamic inference (Tanya)
    • Approximate lifted inference (Kristian)
    • Relational reinforcement learning (Kristian)
  4. Summary (Kristian)

StarAI Lays the Foundation for Artificial Intelligence