FMS: Functional Programming as a Modelling Language

Autor: Ingmar Dasseville, Gerda Janssens

Publikováno v: Functional and Constraint Logic Programming ISBN: 9783030162016
WFLP

In this paper we introduce the Functional Modelling System (FMS). The system introduces the Functional Modelling Language (FML), which is a modelling language for NP-complete search problems based on concepts of functional programming. Internally, we

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7487431a0f113371b996ddbdd1af14d2
https://doi.org/10.1007/978-3-030-16202-3_9

The KB paradigm and its application to interactive configuration

Autor: Ingmar Dasseville, Gerda Janssens, Pieter Van Hertum, Marc Denecker

Publikováno v: Practical Aspects of Declarative Languages ISBN: 9783319282275
PADL

The knowledge base paradigm aims to express domain knowledge in a rich formal language, and to use this domain knowledge as a knowledge base to solve various problems and tasks that arise in the domain by applying multiple forms of inference. As such

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15fa1481c235d702c73b093c962a6d45
https://lirias.kuleuven.be/handle/123456789/540887

Equivalence checking of static affine programs using widening to handle recurrences

Autor: Gerda Janssens, Sven Verdoolaege, Maurice Bruynooghe

Publikováno v: Computer Aided Verification ISBN: 9783642026577
CAV

Designers often apply manual or semi-automatic loop and data transformations on array and loop intensive programs to improve performance. The transformations should preserve the functionality, however, and this paper presents an automatic method for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac16f8d0c3bb56a97bf1ad2c3d44394b
https://doi.org/10.1145/2362389.2362390

Inference and learning in probabilistic logic programs using weighted Boolean formulas

Autor: Dimitar Shterionov, Ingo Thon, Guy Van den Broeck, Luc De Raedt, Bernd Gutmann, Gerda Janssens, Joris Renkens, Daan Fierens

Publikováno v: Theory and Practice of Logic Programming, vol 15, iss 3
Fierens, D; Van Den Broeck, G; Renkens, J; Shterionov, D; Gutmann, B; Thon, I; et al.(2015). Inference and learning in probabilistic logic programs using weighted Boolean formulas. Theory and Practice of Logic Programming, 15(3), 358-401. doi: 10.1017/S1471068414000076. UCLA: Retrieved from: http://www.escholarship.org/uc/item/5zg8p0ff

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b3a98c4e71b14d79ecc8f0bbbca37a3
https://escholarship.org/uc/item/5zg8p0ff

Semantics of templates in a compositional framework for building logics

Autor: Ingmar Dasseville, Marc Denecker, Gerda Janssens, Matthias van der Hallen

There is a growing need for abstractions in logic specification languages such as FO(.) and ASP. One technique to achieve these abstractions are templates (sometimes called macros). While the semantics of templates are virtually always described thro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3814ddbfab8c578e952263cc4a214e28