English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Constraint programming is known for being an efficient approach for solvingcombinatorial problems. Important design choices in a solver are the branchingheuristics, which are designed to lead the search to the best solutions in aminimum amount of time. However, developing these heuristics is atime-consuming process that requires problem-specific expertise. Thisobservation has motivated many efforts to use machine learning to automaticallylearn efficient heuristics without expert intervention. To the best of ourknowledge, it is still an open research question. Although several genericvariable-selection heuristics are available in the literature, the options fora generic value-selection heuristic are more scarce. In this paper, we proposeto tackle this issue by introducing a generic learning procedure that can beused to obtain a value-selection heuristic inside a constraint programmingsolver. This has been achieved thanks to the combination of a deep Q-learningalgorithm, a tailored reward signal, and a heterogeneous graph neural networkarchitecture. Experiments on graph coloring, maximum independent set, andmaximum cut problems show that our framework is able to find better solutionsclose to optimality without requiring a large amounts of backtracks while beinggeneric.

Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver