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Probabilistic logic programming
Information and Computation, 1992 The logic programming language for expressing a probabilistic information is proposed. \(P\)-programs are finite sets of clauses of a special kind: the head of a clause is an atomic formula loaded by a closed interval \([a,b]\), and the body is a set of formulae (not only atomic) which are loaded by closed intervals too. The formula \(F:[a,b]\) denotes
Ng, Raymond, Subrahmanian, V.S.
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Probabilistic abductive logic programming using Dirichlet priors [PDF]
International Journal of Approximate Reasoning, 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.
Broda, K +3 more
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Probabilistic logic programming with conditional constraints
ACM Transactions on Computational Logic, 2001 We introduce a new approach to probabilistic logic programming in which probabilities are defined over a set of possible worlds. More precisely, classical program clauses are extended by a subinterval of [0,1] that describes a range for the conditional probability of the head of a clause given its body.
Thomas Lukasiewicz
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Regularization in Probabilistic Inductive Logic Programming
Lecture Notes in Computer Science, 2023 AbstractProbabilistic Logic Programming combines uncertainty and logic-based languages. Liftable Probabilistic Logic Programs have been recently proposed to perform inference in a lifted way. LIFTCOVER is an algorithm used to perform parameter and structure learning of liftable probabilistic logic programs. In particular, it performs parameter learning
Gentili E. +4 more
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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
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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
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“What if?” in Probabilistic Logic Programming
Theory and Practice of Logic Programming, 2023 AbstractA 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. Intuitively, a ProbLog program defines a distribution by solving a system of equations in terms of mutually independent predefined Boolean random variables.
RAFAEL KIESEL +2 more
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Probabilistic call by push value [PDF]
Logical Methods in Computer Science, 2019 We introduce a probabilistic extension of Levy's Call-By-Push-Value. This extension consists simply in adding a " flipping coin " boolean closed atomic expression. This language can be understood as a major generalization of Scott's PCF encompassing both
Thomas Ehrhard, Christine Tasson
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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.
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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
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