Results 1 to 10 of about 224,550 (312)
Logic + probabilistic programming + causal laws [PDF]
Royal Society Open Science, 2023 Probabilistic planning attempts to incorporate stochastic models directly into the planning process, which is the problem of synthesizing a sequence of actions that achieves some objective for a putative agent.
Vaishak Belle
doaj +8 more sources
Coalgebraic Semantics for Probabilistic Logic Programming [PDF]
Logical Methods in Computer Science, 2021 Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty. It generalises logic programming with the possibility of annotating clauses with probabilities. This paper
Tao Gu, Fabio Zanasi
doaj +10 more sources
MAP Inference for Probabilistic Logic Programming [PDF]
Theory and Practice of Logic Programming, 2020 In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP ...
ELENA BELLODI +3 more
semanticscholar +7 more sources
Meta-analysis of the functional neuroimaging literature with probabilistic logic programming [PDF]
Scientific Reports, 2022 Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis.
Majd Abdallah +3 more
doaj +4 more sources
Neural probabilistic logic programming in DeepProbLog [PDF]
Artificial Intelligence, 2021 We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language.
Robin Manhaeve +4 more
semanticscholar +8 more sources
Statistical Statements in Probabilistic Logic Programming
International Conference on Logic Programming and Non-Monotonic Reasoning, 2022 AbstractProbabilistic Logic Programs under the distribution semantics (PLPDS) do not allow statistical probabilistic statements of the form “90% of birds fly”, which were defined “Type 1” statements by Halpern. In this paper, we add this kind of statements to PLPDS and introduce the PASTA (“Probabilistic Answer set programming for STAtistical ...
Damiano Azzolini +2 more
semanticscholar +4 more sources
Approximate Inference for Neural Probabilistic Logic Programming [PDF]
International Conference on Principles of Knowledge Representation and Reasoning, 2021 DeepProbLog is a neural-symbolic framework that integrates probabilistic logic programming and neural networks. It is realized by providing an interface between the probabilistic logic and the neural networks.
Robin Manhaeve, G. Marra, L. D. Raedt
semanticscholar +7 more sources
"What if?" in Probabilistic Logic Programming [PDF]
Theory and Practice of Logic Programming, 2023 A ProbLog program is a logic program with facts that only hold with a specified probability. In this contribution, we extend this ProbLog language by the ability to answer “What if” queries.
Rafael Kiesel +2 more
semanticscholar +4 more sources
An Asymptotic Analysis of Probabilistic Logic Programming, with Implications for Expressing Projective Families of Distributions [PDF]
Theory and Practice of Logic Programming, 2021 Probabilistic logic programming is a major part of statistical relational artificial intelligence, where approaches from logic and probability are brought together to reason about and learn from relational domains in a setting of uncertainty.
Felix Weitkämper
openalex +3 more sources
onto2problog: A Probabilistic Ontology-Mediated Querying System using Probabilistic Logic Programming. [PDF]
Kunstliche Intell (Oldenbourg), 2020 We present onto2problog, a tool that supports ontology-mediated querying of probabilistic data via probabilistic logic programming engines. Our tool supports conjunctive queries on probabilistic data under ontologies encoded in the description logic ...
van Bremen T, Dries A, Jung JC.
europepmc +2 more sources