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

We consider the problem where an agent wants to find a hidden object that israndomly located in some vertex of a directed acyclic graph (DAG) according toa fixed but possibly unknown distribution. The agent can only examine verticeswhose in-neighbors have already been examined. In this paper, we address alearning setting where we allow the agent to stop before having found theobject and restart searching on a new independent instance of the same problem.Our goal is to maximize the total number of hidden objects found given a timebudget. The agent can thus skip an instance after realizing that it would spendtoo much time on it. Our contributions are both to the search theory andmulti-armed bandits. If the distribution is known, we provide a quasi-optimaland efficient stationary strategy. If the distribution is unknown, weadditionally show how to sequentially approximate it and, at the same time, actnear-optimally in order to collect as many hidden objects as possible.

Finding the bandit in a graph: Sequential search-and-stop