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

Assemblies of modular subsystems are being pressed into service to performsensing, reasoning, and decision making in high-stakes, time-critical tasks insuch areas as transportation, healthcare, and industrial automation. We addressthe opportunity to maximize the utility of an overall computing system byemploying reinforcement learning to guide the configuration of the set ofinteracting modules that comprise the system. The challenge of doingsystem-wide optimization is a combinatorial problem. Local attempts to boostthe performance of a specific module by modifying its configuration often leadsto losses in overall utility of the system's performance as the distribution ofinputs to downstream modules changes drastically. We present metareasoningtechniques which consider a rich representation of the input, monitor the stateof the entire pipeline, and adjust the configuration of modules on-the-fly soas to maximize the utility of a system's operation. We show significantimprovement in both real-world and synthetic pipelines across a variety ofreinforcement learning techniques.

Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations