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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Experimental design in field robotics is an adaptive human-in-the-loopdecision-making process in which an experimenter learns about systemperformance and limitations through interactions with a robot in the form ofconstructed experiments. This can be challenging because of system complexity,the need to operate in unstructured environments, and the competing objectivesof maximizing information gain while simultaneously minimizing experimentalcosts. Based on the successes in other domains, we propose the use of aDecision Support System (DSS) to amplify the human's decision-making abilities,overcome their inherent shortcomings, and enable principled decision-making infield experiments. In this work, we propose common terminology and a six-stagetaxonomy of DSSs specifically for adaptive experimental design of moreinformative tests and reduced experimental costs. We construct and present ourtaxonomy using examples and trends from DSS literature, including worksinvolving artificial intelligence and Intelligent DSSs. Finally, we identifycritical technical gaps and opportunities for future research to direct thescientific community in the pursuit of next-generation DSSs for experimentaldesign.

Taxonomy of A Decision Support System for Adaptive Experimental Design in Field Robotics