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Found 11 papers, 1 papers with code
Probabilistic Neural Circuits
In this paper we introduce probabilistic neural circuits (PNCs), which strike a balance between PCs and neural nets in terms of tractability and expressive power.
Semirings for Probabilistic and Neuro-Symbolic Logic Programming
The field of probabilistic logic programming (PLP) focuses on integrating probabilistic models into programming languages based on logic.
SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge
Large Language Models (LLMs) have demonstrated impressive planning abilities due to their vast "world knowledge".
Top-Down Knowledge Compilation for Counting Modulo Theories
Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF).
Neural Probabilistic Logic Programming in Discrete-Continuous Domains
Probabilistic NeSy focuses on integrating neural networks with both logic and probability theory, which additionally allows learning under uncertainty.
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains
The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes much of its success to a declarative semantics, the so-called distribution semantics.
Measure Theoretic Weighted Model Integration
Weighted model counting (WMC) is a popular framework to perform probabilistic inference with discrete random variables.
Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring
To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations.
Monte Carlo Anti-Differentiation for Approximate Weighted Model Integration
For both of these problems inference techniques have been developed separately in order to manage their #P-hardness, such as knowledge compilation for WMC and Monte Carlo (MC) methods for (approximate) integration in the continuous domain.
Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot's world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human.
Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming
In probabilistic reasoning, the traditionally discrete domain has been elevated to the hybrid domain encompassing additionally continuous random variables.
