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Found 53 papers, 12 papers with code
Mapping probability word problems to executable representations
While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically.
Have Large Language Models Learned to Reason? A Characterization via 3-SAT Phase Transition
To study the reasoning capabilities in a principled fashion, we adopt a computational theory perspective and propose an experimental protocol centered on 3-SAT -- the prototypical NP-complete problem lying at the core of logical reasoning and constraint satisfaction tasks.
Valid Text-to-SQL Generation with Unification-based DeepStochLog
We propose a neurosymbolic framework that imposes SQL syntax and schema constraints with unification-based definite clause grammars and thus guarantees the generation of valid queries.
Neurosymbolic Decision Trees
Neurosymbolic (NeSy) AI studies the integration of neural networks (NNs) and symbolic reasoning based on logic.
The Gradient of Algebraic Model Counting
Concretely, we show that the very same semiring perspective of algebraic model counting also applies to learning.
Relational Neurosymbolic Markov Models
Sequential problems are ubiquitous in AI, such as in reinforcement learning or natural language processing.
NeSyA: Neurosymbolic Automata
Neurosymbolic Artificial Intelligence (NeSy) has emerged as a promising direction to integrate low level perception with high level reasoning.
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic
Neural probabilistic logic systems follow the neuro-symbolic (NeSy) paradigm by combining the perceptive and learning capabilities of neural networks with the robustness of probabilistic logic.
Can Large Language Models Reason? A Characterization via 3-SAT
To study the reasoning capabilities in a principled fashion, we adopt a computational theory perspective and propose an experimental protocol centered on 3-SAT -- the prototypical NP-complete problem lying at the core of logical reasoning and constraint satisfaction tasks.
On the Hardness of Probabilistic Neurosymbolic Learning
The limitations of purely neural learning have sparked an interest in probabilistic neurosymbolic models, which combine neural networks with probabilistic logical reasoning.
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments
In CLEVR-POC, knowledge in the form of logical constraints needs to be leveraged to generate plausible answers to questions about a hidden object in a given partial scene.
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".
Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach
By resorting to a neuro-symbolic approach, DERRL combines relational representations and constraints from symbolic planning with deep learning to extract interpretable policies.
smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation
The first contribution of this paper is a novel interpretation of probabilistic argumentation frameworks as probabilistic logic programs.
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.
Safe Reinforcement Learning via Probabilistic Logic Shields
To this end, we introduce Probabilistic Logic Policy Gradient (PLPG).
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains
While PLP has been extended in various ways for supporting hybrid, that is, mixed discrete and continuous random variables, we are still lacking a declarative semantics for hybrid PLP that not only generalizes the distribution semantics and the modeling language but also the standard inference algorithm that is based on knowledge compilation.
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains
The task then consists of learning this unknown formula from states that are labeled as safe or unsafe by a domain expert.
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation
Combinatorial optimisation problems are ubiquitous in artificial intelligence.
First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs
Three types of independencies are important to represent and exploit for scalable inference in hybrid models: conditional independencies elegantly modeled in Bayesian networks, context-specific independencies naturally represented by logical rules, and independencies amongst attributes of related objects in relational models succinctly expressed by combining rules.
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation
Therefore, the key contribution of this paper are: a more general semantics for ProbLog programs, its implementation into a probabilistic programming framework for both inference and parameter learning, and a novel approach to probabilistic argumentation problems based on such framework.
A Table-Based Representation for Probabilistic Logic: Preliminary Results
We present Probabilistic Decision Model and Notation (pDMN), a probabilistic extension of Decision Model and Notation (DMN).
Shape Inference and Grammar Induction for Example-based Procedural Generation
Designers increasingly rely on procedural generation for automatic generation of content in various industries.
From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey
This survey explores the integration of learning and reasoning in two different fields of artificial intelligence: neurosymbolic and statistical relational artificial intelligence.
Learning Mixed-Integer Linear Programs from Contextual Examples
In this paper, we study the problem of acquiring MILPs from contextual examples, a novel and realistic setting in which examples capture solutions and non-solutions within a specific context.
DeepStochLog: Neural Stochastic Logic Programming
Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard.
Lifted Model Checking for Relational MDPs
We present pCTL-REBEL, a lifted model checking approach for verifying pCTL properties of relational MDPs.
Automating Data Science: Prospects and Challenges
Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.
Leaving Goals on the Pitch: Evaluating Decision Making in Soccer
Our key conclusion is that teams would score more goals if they shot more often from outside the penalty box in a small number of team-specific locations.
Discovering Textual Structures: Generative Grammar Induction using Template Trees
In this paper, we introduce a novel grammar induction algorithm for learning interpretable grammars for generative purposes, called Gitta.
Human-Machine Collaboration for Democratizing Data Science
Everybody wants to analyse their data, but only few posses the data science expertise to to this.
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning.
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.
Neural Probabilistic Logic Programming in DeepProbLog
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates.
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.
Scalable Rule Learning in Probabilistic Knowledge Bases
In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding.
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.
Automating Personnel Rostering by Learning Constraints Using Tensors
Many problems in operations research require that constraints be specified in the model.
DeepProbLog: Neural Probabilistic Logic Programming
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates.
Sketched Answer Set Programming
Answer Set Programming (ASP) is a powerful modeling formalism for combinatorial problems.
Context-based Object Viewpoint Estimation: A 2D Relational Approach
To estimate the viewpoint (or pose) of an object, people have mostly looked at object intrinsic features, such as shape or appearance.
Flexible constrained sampling with guarantees for pattern mining
Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy.
Semiring Programming: A Declarative Framework for Generalized Sum Product Problems
To solve hard problems, AI relies on a variety of disciplines such as logic, probabilistic reasoning, machine learning and mathematical programming.
The Inductive Constraint Programming Loop
Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems.
Graph Invariant Kernels
Vertices of the subgraphs are then compared by a kernel that combines the similarity of their labels and the similarity of their structural role, using a suitable vertex invariant.
Ranked #1 on
Graph Classification
on FRANKENSTEIN
Lazy Explanation-Based Approximation for Probabilistic Logic Programming
We introduce a lazy approach to the explanation-based approximation of probabilistic logic programs.
Exploring the efficacy of molecular fragments of different complexity in computational SAR modeling
An important first step in computational SAR modeling is to transform the compounds into a representation that can be processed by predictive modeling techniques.
Inference and learning in probabilistic logic programs using weighted Boolean formulas
This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs.
kLog: A Language for Logical and Relational Learning with Kernels
The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification.
Bayesian Logic Programs
Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations.
