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

Predicting the outcomes of cyber-physical systems with multiple humaninteractions is a challenging problem. This article reviews a game theoreticalapproach to address this issue, where reinforcement learning is employed topredict the time-extended interaction dynamics. We explain that the mostattractive feature of the method is proposing a computationally feasibleapproach to simultaneously model multiple humans as decision makers, instead ofdetermining the decision dynamics of the intelligent agent of interest andforcing the others to obey certain kinematic and dynamic constraints imposed bythe environment. We present two recent exploitations of the method to model 1)unmanned aircraft integration into the National Airspace System and 2) highwaytraffic. We conclude the article by providing ongoing and future work aboutemploying, improving and validating the method. We also provide related openproblems and research opportunities.

Modeling Cyber-Physical Human Systems via an Interplay Between Reinforcement Learning and Game Theory