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

We study the problem of designing AI agents that can robustly cooperate withpeople in human-machine partnerships. Our work is inspired by real-lifescenarios in which an AI agent, e.g., a virtual assistant, has to cooperatewith new users after its deployment. We model this problem via a parametric MDPframework where the parameters correspond to a user's type and characterize herbehavior. In the test phase, the AI agent has to interact with a user ofunknown type. Our approach to designing a robust AI agent relies on observingthe user's actions to make inferences about the user's type and adapting itspolicy to facilitate efficient cooperation. We show that without beingadaptive, an AI agent can end up performing arbitrarily bad in the test phase.We develop two algorithms for computing policies that automatically adapt tothe user in the test phase. We demonstrate the effectiveness of our approach insolving a two-agent collaborative task.

Towards Deployment of Robust AI Agents for Human-Machine Partnerships