10 papers · 1 filter
Deep probabilistic logic programming for diagnostic reasoning from incomplete information: A case study in stroke detection
Felix Weitkämper, Monchito Avila, Elizabeth Nanjala +2
In medical applications, raw data is frequently associated with significant privacy concerns, lending particular importance to the encoding of summary statistics from the literatur…
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 RückschloÃ, 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…
How Rules Represent Causal Knowledge: Causal Modeling with Abductive Logic Programs
Kilian RückschloÃ, Felix Weitkämper
Pearl observes that causal knowledge enables predicting the effects of interventions, such as actions, whereas descriptive knowledge only permits drawing conclusions from observati…
Projectivity revisited
Felix Weitkämper
The behaviour of statistical relational representations across differently sized domains has become a focal area of research from both a modelling and a complexity viewpoint.Recent…