[TMP-005] A model and architecture for multi-context, value-aware agents
A model of values preferences for AI agents to address simultaneous multiple layered contexts in which they are situated.
"Research bundles" give you a space in the AI on-demand platform where you can collect and publish the outputs of a small research project in a compact way. A research bundle collects in a single place all the assets (code, data, tutorials, examples, ...) produced by your project and published on the AI on-demand platform. Of course, you can also put links to assets published elsewhere, like Github or Zenodo.
A model of values preferences for AI agents to address simultaneous multiple layered contexts in which they are situated.
This paper reviews XR-AI research, analyzing 311 studies, identifying five topics, datasets, tools, and 13 future research opportunities.
A study of consensus building under two different hypotheses: truthful annotators (as a model for most voluntary citizen science projects) and self-interested annotators (as a model for paid crowdsourcing projects).
Our project integrates fair and explainable AI, developing a tool to reduce algorithmic bias through user feedback and iterative refinement.
This project developed an ethical chatbot integrating AI and legal expertise to guide asylum seekers while ensuring transparency and privacy.
This project enhanced ASR systems for dysarthric and stuttering speech using adaptive AI and human-in-the-loop methods.
The project explores complex Human-AI systems, focusing on static and dynamic behaviors, parameter changes, and higher-order interactions for deeper understanding.
The project develops an AI-driven XR aviation assistant to enhance situational awareness in general aviation through 3D visualizations and pilot feedback.
This project develops a cooperative Bayesian optimization game, exploring AI's role in mitigating human biases during interactive decision-making tasks.