Stochastic Logic Programs

By Stephen H. Muggleton. 1996

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Neural Logic Machines (2019, background): “Inductive logic programming (ILP) (Muggleton, 1991; 1996; Fried-man et al., 1999) is a paradigm for learning logic rules derived from a limited set of rule templates from examples.”
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, methodology): “The idea is based on stochastic logic programs [31], and soft logic is applied to transform the hypothesis search into a chain of matrix operations.”
Church: a language for generative models (2008, background): “…unlike several widely used languages grounded in propositional logic (e.g. BUGS [9]) and ﬁrst-order logic (e.g. the logic programming approaches of [13, 19], BLOG [12], and Markov logic [18]), generative processes in Church are ﬁrst-class objects that can be arbitrarily composed and abstracted.”
Automated Variational Inference in Probabilistic Programming (2013, methodology): “Examples include BLOG [1], PRISM [2], Bayesian Logic Programs [3], Stochastic Logic Programs [4], Independent Choice Logic [5], IBAL [6], Probabilistic Scheme [7], Λ ◦ [8], Church [9], Stochastic Matlab [10], and HANSEI [11].”
Reading comprehension tests for computer-based understanding evaluation (2005, background): “An interesting and elegant approach to discriminate among multiple proofs is Stochastic Logic Programming (SLP) described in (Muggleton 1996).”
Applications of Probabilistic Programming (Master's thesis, 2015) (2016, methodology): “Approaches to program synthesis include work in inductive logic programming (Muggleton, 1996; Kersting, 2005; Raedt et al., 2008; Lin et al., 2014), evolutionary programming (Koza, 1992), inference over grammars (Olsson, 1995), and functional programming (Schmid and Wysotzki, 1998).”
Inferring textual entailment with a probabilistically sound calculus* (2009, background): “As one of the reviewers pointed out, how our approach relates to stochastic logic programming (SLP; see Muggleton 1996) is an interesting question.”
Uniform Coherence (2016, background): “For more on this subject, refer to [22], [23], [24], [25], [26], [27], [28], [29].”
Probabilistic models with unknown objects (2006, background): “One is stochastic logic programs (SLPs) [Muggleton, 1996].”
Probabilistic (logic) programming concepts (2015, background): “multi-ary random switches of PRISM (Sato and Kameya, 2001), the probabilistic clauses of stochastic logic programs (SLPs) (Muggleton, 1996), and the annotated disjunctions of logic programs with annotated disjunctions (LPADs) (Vennekens et al, 2004), or the CP-events of CP-logic (Vennekens, 2007).”
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling (2001, background): “In SLPs, P(A) and P(A ^ A) do not necessarily coincide because A and A^A may have di erent refutations (Muggleton, 1996; Cussens, 1999, 2001).”
Probabilistic logic programs: Unifying program trace and possible world semantics (2017, background): “, [18], [19], in which case every possible world admits at most one trace, or through symbolic extensions of traces that restore this property in the presence of non-determinism, e.”
Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation (2011, methodology): “Examples include BLOG [7], PRISM [15], Bayesian Logic Programs [5] Stochastic Logic Programs [9], Markov Logic [14], Independent Choice Logic [12], IBAL [11], Proba-bilistic Scheme [13], § ± [10], and HANSEI [6].”
TensorLog: Deep Learning Meets Probabilistic DBs (2017, background): “SLPs were originally deﬁned (Muggleton et al., 1996) for a fairly expressive class of logic programs, namely all programs which are fail free , in the sense that there are no “dead ends” in the proof graph (i.e., from every vertex v , at least one solution node is reachable).”