3 citations · 3 across the 11 of their papers we have counts for
6 papers · 1 filter
From probability to causality in probabilistic logic programming
Zora Wurm, Kilian Rückschloß, Felix Weitkämper
Probabilistic logic programming is a formalism of statistical relational artificial intelligence that supports causal queries, including interventions from outside the system. When…
Logic Programming Semantics for Causal Processes
Felix Weitkämper
Motivated by challenging modelling issues in the life sciences, we investigate the relationship between logic programming semantics and the eventual states of causal processes comp…
How Artificial Intelligence Leads to Knowledge Why: An Inquiry Inspired by Aristotle's Posterior Analytics
Guus Eelink, Kilian Rückschloß, Felix Weitkämper
Bayesian networks and causal models provide frameworks for handling queries about external interventions and counterfactuals, enabling tasks that go beyond what probability distrib…
Understanding Domain-Size Generalization in Markov Logic Networks
Florian Chen, Felix Weitkämper, Sagar Malhotra
We study the generalization behavior of Markov Logic Networks (MLNs) across relational structures of different sizes. Multiple works have noticed that MLNs learned on a given domai…
The generalised distribution semantics and projective families of distributions
Felix Weitkämper
We generalise the distribution semantics underpinning probabilistic logic programming by distilling its essential concept, the separation of a free random component and a determini…
Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies
Felix Weitkämper
Dependencies on the relative frequency of a state in the domain are common when modelling probabilistic dependencies on relational data. For instance, the likelihood of a school cl…