Mustafa Mohsen | Searcharxiv

arXiv2026

Tempus fugit: Anyone can understand temporal logic if they have to save the realm

Often, the easiest way to learn something is to have to use it for a purpose. This purpose can be playful: In 'Tempus fugit', the player takes on the role of a magician who has to defeat enemies by casting spells. The applicability of spells and enemy attacks depends on the truth of formulas in linear temporal logic with past with respect to a trace that the player gradually builds. So, whoever wants to save the realm from monsters has to learn to read logic formulas. This paper describes the small browser game and explains our design choices. We expose how game mechanics connect to linear temporal logic with past over finite traces, and how this can help players approach a daunting topic like formal logic.

cs.LO↗

arXiv2024

Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders

Nowadays, the need for causal discovery is ubiquitous. A better understanding of not just the stochastic dependencies between parts of a system, but also the actual cause-effect relations, is essential for all parts of science. Thus, the need for reliable methods to detect causal directions is growing constantly. In the last 50 years, many causal discovery algorithms have emerged, but most of them are applicable only under the assumption that the systems have no feedback loops and that they are causally sufficient, i.e. that there are no unmeasured subsystems that can affect multiple measured variables. This is unfortunate since those restrictions can often not be presumed in practice. Feedback is an integral feature of many processes, and real-world systems are rarely completely isolated and fully measured. Fortunately, in recent years, several techniques, that can cope with cyclic, causally insufficient systems, have been developed. And with multiple methods available, a practical application of those algorithms now requires knowledge of the respective strengths and weaknesses. Here, we focus on the problem of causal discovery for sparse linear models which are allowed to have cycles and hidden confounders. We have prepared a comprehensive and thorough comparative study of four causal discovery techniques: two versions of the LLC method [10] and two variants of the ASP-based algorithm [11]. The evaluation investigates the performance of those techniques for various experiments with multiple interventional setups and different dataset sizes.

cs.LG↗