Scalable Web Reasoning Using Logic Programming Techniques

2009 ◽  
pp. 102-117
Author(s):  
Gergely Lukácsy ◽  
Péter Szeredi
Keyword(s):  
Logic Programming ◽  
Programming Techniques

scholarly journals In Praise of Impredicativity: A Contribution to the Formalization of Meta-Programming

2019 ◽  
Vol 20 (1) ◽  
pp. 99-146 ◽  
Author(s):  
FRANÇOIS BRY
Keyword(s):  
Logic Programming ◽  
Functional Programming ◽  
Predicate Logic ◽  
Order Logic ◽  
Declarative Programming ◽  
Conservative Extension ◽  
First Order ◽  
Meta Programming ◽  
Programming Techniques ◽  
Higher Order Functions

AbstractProcessing programs as data is one of the successes of functional and logic programming. Higher-order functions, as program-processing programs are called in functional programming, and meta-programs, as they are called in logic programming, are widespread declarative programming techniques. In logic programming, there is a gap between the meta-programming practice and its theory: The formalizations of meta-programming do not explicitly address its impredicativity and are not fully adequate. This article aims at overcoming this unsatisfactory situation by discussing the relevance of impredicativity to meta-programming, by revisiting former formalizations of meta-programming, and by defining Reflective Predicate Logic, a conservative extension of first-order logic, which provides a simple formalization of meta-programming.

scholarly journals Online learning of event definitions

2016 ◽  
Vol 16 (5-6) ◽  
pp. 817-833 ◽  
Author(s):  
NIKOS KATZOURIS ◽  
ALEXANDER ARTIKIS ◽  
GEORGIOS PALIOURAS
Keyword(s):  
Online Learning ◽  
Logic Programming ◽  
Inductive Logic Programming ◽  
Learning Strategy ◽  
Inductive Logic ◽  
Event Recognition ◽  
Event Calculus ◽  
Training Time ◽  
Learning Techniques ◽  
Programming Techniques

AbstractSystems for symbolic event recognition infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Inductive Logic Programming (ILP). We present an ILP system for online learning of Event Calculus theories. To allow for a single-pass learning strategy, we use the Hoeffding bound for evaluating clauses on a subset of the input stream. We employ a decoupling scheme of the Event Calculus axioms during the learning process, that allows to learn each clause in isolation. Moreover, we use abductive-inductive logic programming techniques to handle unobserved target predicates. We evaluate our approach on an activity recognition application and compare it to a number of batch learning techniques. We obtain results of comparable predicative accuracy with significant speed-ups in training time. We also outperform hand-crafted rules and match the performance of a sound incremental learner that can only operate on noise-free datasets.

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