Logic Programming
Probabilistic abductive logic programming using Dirichlet priors
... Probabilistic programming is an area of research that aims to develop general inference algorithms for probabilistic mod- els expressed as probabilistic programs whose execution corresponds to inferring the ...
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Using Screencasts to Enhance Coding Skills: the case of Logic Programming
... a logic programme is not developed sequentially is rather difficult to be presented with Pow- erPoint animations and be accompanied with ...our Logic Programming course, offered in the second year of ...
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Efficient Program Synthesis Using Constraint Satisfaction in Inductive Logic Programming
... We present NrSample, a framework for program synthesis in inductive logic programming. NrSam- ple uses propositional logic constraints to exclude undesirable candidates from the search. This is ...
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Logic Programming From the Point of View of Type Theory and Predicate Linear Logic
... In the 1980s, Dale Miller and GopalanNadathur[5]created a new, wider theoretical basis for logic programming languages.They createdλProlog, which is programming language based on Prolog. λProlog ...
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Applying Constraint Logic Programming to SQL Test Case Generation
... Abstract. We present a general framework for generating SQL query test cases using Constraint Logic Programming. Given a database schema and a SQL view defined in terms of other views and schema tables, our ...
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Cloud Logic Programming for Integrating Language Technology Resources
... We are of course aware of the fact that Prolog has fallen out of fashion in NLP circles since its heyday in the 1980’s. One likely reason for this (but not the only reason) is the dominance of statistical approaches to ...
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Representing and Querying Multiple Ontologies with Contextual Logic Programming
... Contextual Logic Programming as a computational hub for representing and reasoning over knowledge modeled by web ontologies, integrating the approach with similar mechanisms which we already ...its ...
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Logic Programming In Radial Basis Function Neural Networks
... represent logic programs by representing their associated immediate consequence ...the logic programming can be approximated arbitrarily well by MLPs, but do not specify any means for actually ...
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An Entity Mention Model for Coreference Resolution with Inductive Logic Programming
... This paper presented an expressive entity-mention model for coreference resolution by using Inductive Logic Programming. In contrast to the traditional mention-pair model, our model can capture infor- ...
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A Logic Programming View of Relational Morphology
... A Logic Programming View of Relational Morphology A Logic Programming View of Relational Morphology H a r v e y A b r a m s o n I n s t i t u t e o f I n d u s t r i a l S c i e n c e , U n i v e r s[.] ...
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An Empirical Study of the Use of Relevance Information in Inductive Logic Programming
... Inductive Logic Programming (ILP) systems construct models for data using domain-specific back- ground information. When using these systems, it is typically assumed that sufficient human exper- tise is at ...
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A Preliminary Report of Integrating Science and Computing Teaching Using Logic Programming
... models. Logic Programming plays an essential role in making this integration seamless and natural, and also makes it accessible to middle school ...class, Logic Programming based approach is ...
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Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach
... In this work, we present a Chinese dependency parsing method for Weibo, based on efficient prob- abilistic first-order logic programming (Wang et al., 2013). The advantage of probabilistic pro- gramming for ...
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Distributional logic programming for Bayesian knowledge representation
... fits logic programming well and local probabilistic preferences such as control- ling the average size of (BN) ...structure. Logic for excluding invalid edges can be added either progressively via ...
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Advances in integrative statistics for logic programming
... to logic programming, providing access to a popular statisti- cal system that has complementary strengths in areas such as machine learning, statistical inference and ...
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Logic programming and partial deduction for the verification of reactive systems: An experimental evaluation
... Abstract. In earlier work it has been shown that finite state CTL model checking of reactive systems can be achieved by a relatively simple in- terpreter written in tabled logic programming. This approach ...
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Incorporating Linguistics Constraints into Inductive Logic Programming
... In James Cussens and Alan Frisch, editors, Proceed- ings of the lOth International Conference on In- ductive Logic Programming, pages 130-146, Lon- don, August.. Stephen Muggleton and Wr[r] ...
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A Model Checker for Epistemic Hybrid Automata Using Constraints Logic Programming
... constraint logic programming (CLP) [15] model checker for hybrid ...declarative programming language as well as contains efficient constraints solvers of various ...
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MoL 2007 06: Formalizing Implicatures Using Extended Logic Programming
... in logic programming is usually restricted to Herbrand interpretations and models, a program and its grounded version can be used ...of logic programs on the one side and models of the program on the ...
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Inferring Syntactic Rules for Word Alignment through Inductive Logic Programming
... This paper presents and evaluates an original approach to automatically align bitexts at the word level. It relies on a syntactic dependency analysis of the source and target texts and uses a machine-learning technique, ...
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