5 papers
NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning
Daniel Romero Schellhorn, Till Mossakowski, Björn Gehrke
Neurosymbolic semantics is fragmented: classical, fuzzy, probabilistic and neural systems each define truth by their own inductive rules. NeSyCat, extending ULLER, subsumes them un…
NeSyCat: A Monad-Based Categorical Semantics of the Neurosymbolic ULLER Framework
Daniel Romero Schellhorn, Till Mossakowski
ULLER (Unified Language for LEarning and Reasoning) offers a unified first-order logic (FOL) syntax, enabling its knowledge bases to be used directly across a wide range of neurosy…
The Possibility of Artificial Intelligence Becoming a Subject and the Alignment Problem
Till Mossakowski, Helena Esther Grass
The prospect of Artificial General Intelligence (AGI) is increasingly driving institutional decisions, and alignment of AGI is a hard problem. The currently dominant AI alignment s…
Advancing Natural Language Formalization to First Order Logic with Fine-tuned LLMs
Felix Vossel, Till Mossakowski, Björn Gehrke
Automating the translation of natural language to first-order logic (FOL) is crucial for knowledge representation and formal methods, yet remains challenging. We present a systemat…
ChemLog: Making MSOL Viable for Ontological Classification and Learning
Simon Flügel, Martin Glauer, Till Mossakowski +1
Despite its prevalence, in many domains, OWL is not expressive enough to define ontology classes. In this paper, we present an approach that allows to use monadic second-order form…