Learning MAX-SAT from contextual examples for combinatorial optimisation

, , &
Artificial Intelligence 314 (C):103794 (2023)
  Copy   BIBTEX

Abstract

This article has no associated abstract. (fix it)

Categories

Keywords

Reprint years

DOI

10.1016/j.artint.2022.103794

Other Versions

No versions found

Links

PhilArchive

    This entry is not archived by us. If you are the author and have permission from the publisher, we recommend that you archive it. Many publishers automatically grant permission to authors to archive pre-prints. By uploading a copy of your work, you will enable us to better index it, making it easier to find.

    Upload a copy of this work     Papers currently archived: 106,168

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

My notes

Similar books and articles

Learning action models from plan examples using weighted MAX-SAT.Qiang Yang, Kangheng Wu & Yunfei Jiang - 2007 - Artificial Intelligence 171 (2-3):107-143.
Resolution for Max-SAT.María Luisa Bonet, Jordi Levy & Felip Manyà - 2007 - Artificial Intelligence 171 (8-9):606-618.
A logical approach to efficient Max-SAT solving.Javier Larrosa, Federico Heras & Simon de Givry - 2008 - Artificial Intelligence 172 (2-3):204-233.
Understanding the power of Max-SAT resolution through UP-resilience.Mohamed Sami Cherif, Djamal Habet & André Abramé - 2020 - Artificial Intelligence 289 (C):103397.
On the power of clause-learning SAT solvers as resolution engines.Knot Pipatsrisawat & Adnan Darwiche - 2011 - Artificial Intelligence 175 (2):512-525.
Learning logic programs with structured background knowledge☆☆An extended abstract of this paper appeared in: L. De Raedt (Ed.), Proceedings of the Fifth International Workshop on Inductive Logic Programming, Tokyo, Japan, 1995, pp. 53–76, Scientific Report of the Department of Computer Science, Katholieke Universiteit Leuven, and also in the post-conference volume: L. De Raedt (Ed.), Advances in Inductive Logic Programming, IOS Press, Amsterdam/Ohmsha, Tokyo, 1996, pp. 172–191. [REVIEW]Tamás Horváth & György Turán - 2001 - Artificial Intelligence 128 (1-2):31-97.
Learning production systems from examples.Charles L. Hedrick - 1976 - Artificial Intelligence 7 (1):21-49.
Noise modelling and evaluating learning from examples.Ray J. Hickey - 1996 - Artificial Intelligence 82 (1-2):157-179.
Learning and applying contextual constraints in sentence comprehension.Mark F. St John & James L. McClelland - 1990 - Artificial Intelligence 46 (1-2):217-257.
Stefano Franchi and Güven Güzeldere, eds., Mechanical Bodies, Computational Minds: Artificial Intelligence from Automata to Cyborgs Reviewed by.John Sutton - 2006 - Philosophy in Review 26 (6):414-416.

Analytics

Added to PP
2022-10-26

Downloads
18 (#1,214,179)

6 months
3 (#1,170,603)

Historical graph of downloads
How can I increase my downloads?

Author Profiles

Mohit Kumar
University of Western Australia
Luc De Raedt

Citations of this work

Add more citations

References found in this work

The TSP phase transition.Ian P. Gent & Toby Walsh - 1996 - Artificial Intelligence 88 (1-2):349-358.
Structured learning modulo theories.Stefano Teso, Roberto Sebastiani & Andrea Passerini - 2017 - Artificial Intelligence 244 (C):166-187.

Add more references