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Found 6 papers, 4 papers with code
ULLER: A Unified Language for Learning and Reasoning
The field of neuro-symbolic artificial intelligence (NeSy), which combines learning and reasoning, has recently experienced significant growth.
logLTN: Differentiable Fuzzy Logic in the Logarithm Space
A significant trend in the literature involves integrating axioms and facts in loss functions by grounding logical symbols with neural networks and operators with fuzzy semantics.
KitchenScale: Learning to predict ingredient quantities from recipe contexts
We introduce KitchenScale, a fine-tuned Pre-trained Language Model (PLM) that predicts a target ingredient's quantity and measurement unit given its recipe context.
Interval Logic Tensor Networks
In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of real-featured data.
Logic Tensor Networks
In this paper, we present Logic Tensor Networks (LTN), a neurosymbolic formalism and computational model that supports learning and reasoning through the introduction of a many-valued, end-to-end differentiable first-order logic called Real Logic as a representation language for deep learning.
Injecting Prior Knowledge for Transfer Learning into Reinforcement Learning Algorithms using Logic Tensor Networks
Facts are provided as background knowledge a priori to learning a policy for how to act in the world.
