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

Much of the recent work developing formal methods techniques to specify orlearn the behavior of autonomous systems is predicated on a belief that formalspecifications are interpretable and useful for humans when checking systems.Though frequently asserted, this assumption is rarely tested. We performed ahuman experiment (N = 62) with a mix of people who were and were not familiarwith formal methods beforehand, asking them to validate whether a set of signaltemporal logic (STL) constraints would keep an agent out of harm and allow itto complete a task in a gridworld capture-the-flag setting. Validation accuracywas $45\% \pm 20\%$ (mean $\pm$ standard deviation). The ground-truth validityof a specification, subjects' familiarity with formal methods, and subjects'level of education were found to be significant factors in determiningvalidation correctness. Participants exhibited an affirmation bias, causingsignificantly increased accuracy on valid specifications, but significantlydecreased accuracy on invalid specifications. Additionally, participants,particularly those familiar with formal methods, tended to be overconfident intheir answers, and be similarly confident regardless of actual correctness.  Our data do not support the belief that formal specifications are inherentlyhuman-interpretable to a meaningful degree for system validation. We recommendergonomic improvements to data presentation and validation training, whichshould be tested before claims of interpretability make their way back into theformal methods literature.

STL: Surprisingly Tricky Logic (for System Validation)