How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

From the latter half of the last decade, there has been a growing interest in developing
algorithms for automatically solving mathematical word problems (MWP). It is a challenging …

The field of probabilistic logic programming (PLP) focuses on integrating probabilistic
models into programming languages based on logic. Over the past 30 years, numerous …

From the latter half of the last decade, there has been a growing interest in developing
algorithms for automatically solving mathematical word problems (MWP). It is a challenging …

Combinatorics math problems are often used as a benchmark to test human cognitive and
logical problem-solving skills. These problems are concerned with counting the number of …

Words of estimative probability (WEP) are expressions of a statement's plausibility (probably,
maybe, likely, doubt, likely, unlikely, impossible...). Multiple surveys demonstrate the …

We study the problem of generating interesting integer sequences with a combinatorial
interpretation. For this we introduce a two-step approach. In the first step, we generate first …

The journey captured by this dissertation centers around knowledge compilation, model
counting, and their role within state-of-the-art inference algorithms for probabilistic logic …

Tato práce cíívá predikátovou logiku prvního řádu pro reprezentaci dat i modelů.
Neoddiskutovatelnou výhodou tohoto formalismu je snadné vysvětlení naučených modelů a …

Relační učení využívá predikátovou logiku prvního řádu pro reprezentaci dat i modelů.
Neoddiskutovatelnou výhodou tohoto formalismu je snadné vysvětlení naučených modelů a …