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

Dynamic difficulty adjustment ($DDA$) is a process of automatically changinga game difficulty for the optimization of user experience. It is a vital partof almost any modern game. Most existing DDA approaches concentrate on theexperience of a player without looking at the rest of the players. We propose amethod that automatically optimizes user experience while taking intoconsideration other players and macro constraints imposed by the game. Themethod is based on deep neural network architecture that involves a count lossconstraint that has zero gradients in most of its support. We suggest a methodto optimize this loss function and provide theoretical analysis for itsperformance. Finally, we provide empirical results of an internal experimentthat was done on $200,000$ players and was found to outperform thecorresponding manual heuristics crafted by game design experts.

DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints