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

Monte Carlo Search gives excellent results in multiple difficultcombinatorial problems. Using a prior to perform non uniform playouts duringthe search improves a lot the results compared to uniform playouts. Handmadeheuristics tailored to the combinatorial problem are often used as priors. Wepropose a method to automatically compute a prior. It uses statistics on solvedproblems. It is a simple and general method that incurs no computational costat playout time and that brings large performance gains. The method is appliedto three difficult combinatorial problems: Latin Square Completion, Kakuro, andInverse RNA Folding.

Learning a Prior for Monte Carlo Search by Replaying Solutions to Combinatorial Problems