Syntactic Requirements for Well-defined Hybrid Probabilistic Logic
Programs
- URL: http://arxiv.org/abs/2109.08283v1
- Date: Fri, 17 Sep 2021 01:45:34 GMT
- Title: Syntactic Requirements for Well-defined Hybrid Probabilistic Logic
Programs
- Authors: Damiano Azzolini, Fabrizio Riguzzi
- Abstract summary: Hybrid probabilistic logic programs can represent several scenarios thanks to the expressivity of Logic Programming extended with facts representing discrete and continuous distributions.
Here, following one recent semantics proposal, we illustrate a concrete syntax, and we analyse the syntactic requirements needed to preserve the well-definedness.
- Score: 0.5076419064097734
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Hybrid probabilistic logic programs can represent several scenarios thanks to
the expressivity of Logic Programming extended with facts representing discrete
and continuous distributions. The semantics for this type of programs is
crucial since it ensures that a probability can be assigned to every query.
Here, following one recent semantics proposal, we illustrate a concrete syntax,
and we analyse the syntactic requirements needed to preserve the
well-definedness.
Related papers
- Symbolic Parameter Learning in Probabilistic Answer Set Programming [0.16385815610837165]
We propose two algorithms to solve the formalism of Proabilistic Set Programming.
The first solves the task using an off-the-shelf constrained optimization solver.
The second is based on an implementation of the Expectation Maximization algorithm.
arXiv Detail & Related papers (2024-08-16T13:32:47Z) - The Foundations of Tokenization: Statistical and Computational Concerns [51.370165245628975]
Tokenization is a critical step in the NLP pipeline.
Despite its recognized importance as a standard representation method in NLP, the theoretical underpinnings of tokenization are not yet fully understood.
The present paper contributes to addressing this theoretical gap by proposing a unified formal framework for representing and analyzing tokenizer models.
arXiv Detail & Related papers (2024-07-16T11:12:28Z) - $\omega$PAP Spaces: Reasoning Denotationally About Higher-Order,
Recursive Probabilistic and Differentiable Programs [64.25762042361839]
$omega$PAP spaces are spaces for reasoning denotationally about expressive differentiable and probabilistic programming languages.
Our semantics is general enough to assign meanings to most practical probabilistic and differentiable programs.
We establish the almost-everywhere differentiability of probabilistic programs' trace density functions.
arXiv Detail & Related papers (2023-02-21T12:50:05Z) - The generalised distribution semantics and projective families of distributions [0.0]
We generalise the distribution semantics underpinning probabilistic logic programming by distilling its essential concept, the separation of a free random component and a deterministic part.
This abstracts the core ideas beyond logic programming to encompass frameworks from probabilistic databases, probabilistic finite model theory and discrete lifted Bayesian networks.
arXiv Detail & Related papers (2022-11-12T21:44:22Z) - Semantic Probabilistic Layers for Neuro-Symbolic Learning [83.25785999205932]
We design a predictive layer for structured-output prediction (SOP)
It can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints.
Our Semantic Probabilistic Layer (SPL) can model intricate correlations, and hard constraints, over a structured output space.
arXiv Detail & Related papers (2022-06-01T12:02:38Z) - Distributional Gradient Boosting Machines [77.34726150561087]
Our framework is based on XGBoost and LightGBM.
We show that our framework achieves state-of-the-art forecast accuracy.
arXiv Detail & Related papers (2022-04-02T06:32:19Z) - SMProbLog: Stable Model Semantics in ProbLog and its Applications in
Argumentation [17.71804768917815]
SMProbLog is a generalization of the probabilistic logic programming language ProbLog.
We show how SMProbLog can be used to reason about probabilistic argumentation problems.
arXiv Detail & Related papers (2021-10-05T12:29:22Z) - Logical Credal Networks [87.25387518070411]
This paper introduces Logical Credal Networks, an expressive probabilistic logic that generalizes many prior models that combine logic and probability.
We investigate its performance on maximum a posteriori inference tasks, including solving Mastermind games with uncertainty and detecting credit card fraud.
arXiv Detail & Related papers (2021-09-25T00:00:47Z) - An asymptotic analysis of probabilistic logic programming with
implications for expressing projective families of distributions [0.0]
We show that every probabilistic logic program under the distribution semantics is relationalally equivalent to a probabilistic logic program.
Range-restricted logic programs correspond to quantifier-free theories, making quantifier results avilable for use.
arXiv Detail & Related papers (2021-02-17T14:07:16Z) - Representing Partial Programs with Blended Abstract Semantics [62.20775388513027]
We introduce a technique for representing partially written programs in a program synthesis engine.
We learn an approximate execution model implemented as a modular neural network.
We show that these hybrid neuro-symbolic representations enable execution-guided synthesizers to use more powerful language constructs.
arXiv Detail & Related papers (2020-12-23T20:40:18Z) - Stochastic Probabilistic Programs [1.90365714903665]
We introduce the notion of a probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of programs and inference in them.
We give several examples of probabilistic programs, and compare the programs with corresponding deterministic probabilistic programs in terms of model specification and inference.
arXiv Detail & Related papers (2020-01-08T17:54:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.