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Found 20 papers, 4 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.
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.
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.
Efficient Knowledge Compilation Beyond Weighted Model Counting
We introduce Second Level Algebraic Model Counting (2AMC) as a generic framework for these kinds of problems.
Using DeepProbLog to perform Complex Event Processing on an Audio Stream
We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.
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.
Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits
When collaborating with an AI system, we need to assess when to trust its recommendations.
Proceedings 36th International Conference on Logic Programming (Technical Communications)
Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming.
A Hybrid Neuro-Symbolic Approach for Complex Event Processing
We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.
Increasing negotiation performance at the edge of the network
In particular, we show our method significantly reduces the number of messages when an agreement is not possible.
The current state of automated negotiation theory: a literature review
Automated negotiation can be an efficient method for resolving conflict and redistributing resources in a coalition setting.
Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs (Technical Report)
State-of-the-art inference approaches in probabilistic logic programming typically start by computing the relevant ground program with respect to the queries of interest, and then use this program for probabilistic inference using knowledge compilation and weighted model counting.
Neural Probabilistic Logic Programming in DeepProbLog
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates.
Probabilistic Logic Programming with Beta-Distributed Random Variables
We enable aProbLog---a probabilistic logical programming approach---to reason in presence of uncertain probabilities represented as Beta-distributed random variables.
DeepProbLog: Neural Probabilistic Logic Programming
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates.
Domain Recursion for Lifted Inference with Existential Quantifiers
In this paper, we show that domain recursion can also be applied to models with existential quantifiers.
A Collective, Probabilistic Approach to Schema Mapping: Appendix
In this appendix we provide additional supplementary material to "A Collective, Probabilistic Approach to Schema Mapping."
New Liftable Classes for First-Order Probabilistic Inference
Statistical relational models provide compact encodings of probabilistic dependencies in relational domains, but result in highly intractable graphical models.
Lazy Explanation-Based Approximation for Probabilistic Logic Programming
We introduce a lazy approach to the explanation-based approximation of probabilistic logic programs.
