Statistical Relational Artificial Intelligence (StaRAI)

Held in winter 22, winter 26, planned for winter 28

Overview

In systems that exhibit artificial intelligence (AI), an agent at its centre has to learn and represent a model of its environment, reason about it, and decide on its actions. A possible approach to realising such a rational agent lies in probabilistic graphical models, which allows for modelling uncertainty. Another approach lies in first-order logics, which allows for modelling objects and relations. Methods from the field of statistical relational AI (StaRAI) combine both aspects, dealing with uncertainty and relations. This lecture focusses on the beginnings as well as the newest developments within StaRAI, covering the different aspects of the tasks an agent has to solve.

This lecture will be revised for the upcoming iteration.

Please register in the Learnweb course for more details and material once it exists.

Presentation Material

The slides used in the lecture always evolve a little bit. The following agenda links the most recent version. In older Learnweb courses you may find older version no longer linked here.

Topics and slides:

  1. Introduction (pptx, pdf)
  2. Modelling Formalisms:
    1. Logic (pptx, pdf)
    2. Probabilistic Graphical Models (pptx, pdf)
    3. Probabilistic Relational Models (pptx, pdf)
  3. Inference
    1. Exact Inference
      1. (Weighted) Model Counting (pptx, pdf)
      2. Variable Elimination and the Like (pptx, pdf)
      3. Lifted Inference (pptx, pdf)
    2. Approximate Inference (pptx, pdf)
  4. Learning (pptx, pdf)
  5. Sequential Models and Inference (pptx, pdf)
  6. Decision-theoretic Models and Inference (pptx, pdf)

Literature

The lecture is heavily based on research papers on the topics mentioned above and will be referenced on the slides. For more a more general introduction and details on propositional modelling and inference, please refer to the following books:

  • Modelling and Reasoning with Bayesian Networks, Adnan Darwiche
  • Probabilistic Graphical Models, Daphne Koller and Nir Friedman
  • Artificial Intelligence - A Modern Approach (3rd ed.), Stuart Russel and Peter Norvig

All sources should be available (i.e., find-able) online.