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A coalgebraic perspective on probabilistic logic programming [PDF]
, 2019 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
Bart Jacobs, Aleks Kissinger
core +6 more sources
Declarative Probabilistic Logic Programming in Discrete-Continuous Domains [PDF]
Artificial Intelligence, 2023 Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes much ...
Pedro Zuidberg Dos Martires +2 more
semanticscholar +1 more source
Bottom-Up Stratified Probabilistic Logic Programming with Fusemate [PDF]
International Conference on Logic Programming, 2023 This paper introduces the Fusemate probabilistic logic programming system. Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference.
Peter Baumgartner, Elena Tartaglia
semanticscholar +1 more source
Probabilistic abductive logic programming using Dirichlet priors [PDF]
, 2016 Probabilistic programming is an area of research that aims to develop general inference algorithms for probabilistic models expressed as probabilistic programs whose execution corresponds to inferring the parameters of those models.
Călin-Rareş Turliuc +3 more
openalex +3 more sources
On the Strong Equivalences for LPMLN Programs [PDF]
Logical Methods in Computer Science, 2021 LPMLN is a powerful knowledge representation and reasoning tool that combines the non-monotonic reasoning ability of Answer Set Programming (ASP) and the probabilistic reasoning ability of Markov Logic Networks (MLN).
Bin Wang +3 more
doaj +1 more source
Probabilistic Logic Models for the Lightning Network
Cryptography, 2022 The Lightning Network (LN) has emerged as one of the prominent solutions to overcome the biggest limit of blockchain based on PoW: scalability. LN allows for creating a layer on top of an existing blockchain where users can send payments and micro ...
Damiano Azzolini, Fabrizio Riguzzi
doaj +1 more source
Developing an optimization model for prioritizing and selecting project risk response strategies [PDF]
Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī, 2023 Projects, during their execution, face various risks that can impact the achievement of project objectives. Therefore, the need for extensive project risk management is widely recognized.
Ali Namazian, Somayeh Behboodian
doaj +1 more source
Intention Recognition With ProbLog
Frontiers in Artificial Intelligence, 2022 In many scenarios where robots or autonomous systems may be deployed, the capacity to infer and reason about the intentions of other agents can improve the performance or utility of the system.
Gary B. Smith +6 more
doaj +1 more source
Optimizing Probabilities in Probabilistic Logic Programs [PDF]
Theory and Practice of Logic Programming, 2021 AbstractProbabilistic logic programming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions of random variables.
Azzolini D., Riguzzi F.
openaire +4 more sources
ProbLog2: Probabilistic Logic Programming [PDF]
, 2015 We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations.
Dries, Anton +6 more
openaire +2 more sources