Algorithmic selection is omnipresent in various domains of our online everyday lives: it ranks search results, curates social media news feeds, or recommends videos to watch and music listen to. This widespread application algorithmic on the internet can be associated with risks like feeling surveilled (S), exposed distorted information (D), one using too excessively (O). One way which users co...

This paper introduces a method for algorithmic reduction of the search space of an ILP task, omitting the need for explicit language bias. It relies on bottom-up propositionalisation of examples and background knowledge. A proof of concept has been developed for observational learning of stratified normal logic programs.

Logical theories in reasoning about actions may also evolve, and knowledge engineers need revision tools to incorporate new incoming laws about the dynamic environment. We here fill this gap by providing an algorithmic approach for action theory revision. We give a well defined semantics that ensures minimal change, and show correctness of our algorithms w.r.t. the semantic constructions.