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Regularization in Probabilistic Inductive Logic Programming
International Conference on Inductive Logic Programming, 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
Elisabetta Gentili +4 more
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Direct design of ground-state probabilistic logic using many-body interactions for probabilistic computing [PDF]
Scientific ReportsIn this work, an innovative design model aimed at enhancing the efficacy of ground-state probabilistic logic with a binary energy landscape (GSPL-BEL) is presented.
Yihan He +3 more
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Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation [PDF]
Theory and Practice of Logic ProgrammingMany inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, for example, coming from sensory data or neural networks with probabilities.
Fieke Hillerström, Gertjan J. Burghouts
semanticscholar +4 more sources
Applied Artificial IntelligenceWe propose probabilistic logic factored Markov decision processes (PL-fMDPs) as a behavior selection scheme for self-driving cars. Probabilistic logic combines logic programming with probability theory to achieve clear, rule-based knowledge descriptions ...
Héctor Avilés +6 more
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Learning hierarchical probabilistic logic programs [PDF]
Machine Learning, 2021 AbstractProbabilistic logic programming (PLP) combines logic programs and probabilities. Due to its expressiveness and simplicity, it has been considered as a powerful tool for learning and reasoning in relational domains characterized by uncertainty. Still, learning the parameter and the structure of general PLP is computationally expensive due to the
Arnaud Nguembang Fadja +2 more
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Probabilistic Disjunctive Logic Programming [PDF]
, 2013 In this paper we propose a framework for combining Disjunctive Logic Programming and Poole's Probabilistic Horn Abduction. We use the concept of hypothesis to specify the probability structure. We consider the case in which probabilistic information is not available.
Liem Viet Ngo
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Ontology-Mediated Queries over Probabilistic Data via Probabilistic Logic Programming
International Conference on Information and Knowledge Management, 2019 We study ontology-mediated querying over probabilistic data for the case when the ontology is formulated in EL(hdr), an expressive member of the EL family of description logics.
Timothy van Bremen +2 more
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Probabilistic Constraint Logic Programming
, 1997 35 pages, uses sfbart ...
Riezler, Stefan
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Explaining Explanations in Probabilistic Logic Programming
Asian Symposium on Programming Languages and SystemsThe emergence of tools based on artificial intelligence has also led to the need of producing explanations which are understandable by a human being. In most approaches, the system is considered a black box, making it difficult to generate appropriate ...
Germ'an Vidal
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Constraints for probabilistic logic programming [PDF]
, 2012 In knowledge representation, one commonly distinguishes definitions of predicates from constraints. This distinction is also useful for probabilistic programming and statistical relational learning as it explains the key differences between probabilistic programming languages such as ICL, ProbLog and Prism (which are based on definitions) and ...
Daan Fierens +3 more
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