Foundations of Logic Programming

By J. Lloyd. 1984

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Citations in the corpus

ConGolog, a concurrent programming language based on the situation calculus (2000, background): “Induction on the number of selected literals of the form trans(P ( ~ t); s 1 ; 2 ; s 2 ) (final(P( ~ t); s 1 )) occurring in a successful SLDNF-derivation, for success.”
Reasoning about Time and Knowledge in Neural Symbolic Learning Systems (2003, background): “In the network, neurons are labelled as ©K1L1 or K1L1 to differentiate the two concepts.”
Composing schema mappings: second-order dependencies to the rescue (2004, methodology): “In particular, we introduce chasing with SO tgds as a variation on logic program evaluation [16] that has polynomial-time computation, and give a proposition that says that its result is a universal solution.”
Probabilistic Inductive Logic Programming (2004, methodology): “For a detailed introduction to logic programming, we refer to [ 33 ], for a more gentle introduction, we refer to [15], and for a detailed discussion of Prolog, see [58].”
Linking Data to Ontologies (2008, methodology): “We briey recall some basic notions from logic programming [ 20 ], upon which we build our unfolding technique.”
On Indicative Conditionals (2015, unknown): “[11]), abduction [9], Stenning and van Lambalgen’s representation of conditionals as well as their semantic operator [19] and three-valued Lukasiewicz logic [12], which has been put together in [6,7,5,8,3] and has been applied to the suppression [2] and the selection task [1], as well as to model th”
The well-founded semantics for general logic programs (1991, background): “The formal details, including handling of variables and introduction of axioms for equality, are described in several places [6, 2, 20, 9, 17].”
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling (2001, methodology): “Likewise comp(R) is a rst-order theory which deductively simulates SLD refutation with the help of E q by replacing a clause head atom with the clause body (Lloyd, 1984; Doets, 1994).”
Under Consideration for Publication in Theory and Practice of Logic Programming Inference and Learning in Probabilistic Logic Programs Using Weighted Boolean Formulas (methodology): “For acyclic rules, the conversion is straightforward, we can simply take Clark's completion of the rules (Lloyd 1987; Janhunen 2004).”
Inverse entailment and progol (1995, background): “Least Herbrand models of logic programs [26] t neatly with the distinction between examples and conjectured theories in inductive inference.”
Parameter Learning in Probabilistic Databases: A Least Squares Approach (2008, methodology): “Calculating the explanation probability can easily be realized using a bestfirst search – guided by the probability of the current derivation – through standard logic programming techniques based on the SLD-tree [18].”
Approximating the Semantics of Logic Programs by Recurrent Neural Networks (1999, methodology): “[ 9 ]),where the correspondence between the least model ofP and the least fixed point of TP can be shown by lattice-theoretic arguments [10], or the class of acceptable programs, where the just mentioned correspondence can be shown using metric methods [11].”
Dimensions of Neural-symbolic Integration - A Structured Survey (2005, methodology): “This “follows-from” is usually captured by the associated consequence operator TP [Lloyd, 1988].”