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

ion Level State Information States Total States FullGoal arrival time 5Goal direction Θ 7 175Goal direction Φ 5LargeGoal arrival time 5Goal direction | Θ | 5 125Goal direction | Φ | 5Small Goal seek priority 5 5Table 1. The different state abstractions used in the development of a composite behaviorusing the G OAL S EEK primitive task are shown.of the angle, such as SMALL. State information not describing a direction remains un-changed. Small In this state space, state information is abstracted into a single dynamic priority whichis calculated using all the relevant state information local to each primitive task. Thisdynamic priority represented the task’s determination of its applicability to the agent’scurrent state. For example, using this state space, the CA-GS-RA composite behaviorwould only use dynamic priorities for the primitive behaviors C OLLISION A VOIDANCE ,G OAL S EEK , and R UN A WAY to determine how to weight its sub-behaviors. While thislevel of abstraction may appear to be too extreme, we have previously shown that rule-setsusingdynamicprioritiescanbedevelopedwhichhavesimilarperformancetothoseusing the

State and Action Abstraction in the Development of Agent Controllers