Periodic Reporting for period 1 - HumAIne (Hybrid Human-AI Decision Support for Enhanced Human Empowerment in Dynamic Situations)
Période du rapport: 2023-10-01 au 2025-03-31
HumAIne is a pioneering initiative dedicated to researching, developing, validating, and promoting a revolutionary platform for Human-AI collaboration. The project aims to empower AI solution integrators by creating a platform that facilitates advanced decision-making applications in dynamic and unstructured environments across various industrial sectors and according to the following three AI paradigms:
- Active Learning: facilitates the development of Human-in-the-Loop systems, involving humans in AI processes when faced with increased uncertainty.
- Neuro-Symbolic Learning: combines Deep Learning with semantics and rules, enabling the completion of complex tasks with high accuracy and minimal training data.
- Swarm Learning: fosters collaborative synergy through a networked swarm of AI agents, elevating collective intelligence for optimal system efficiency.
- Explainable AI (XAI): provides advanced models for predictive explanations, considering global context and allowing dynamic feedback from users.
The Project's Objectives may be summarized as follows:
- Define a standard-based Reference Architecture for collaborative HumAIne systems
- Ensure trustworthy, transparent and interoperable interactions between Ηumans and AI systems
- Accelerate knowledge acquisition based on Active Learning
- Enable collective intelligence for Humans and AI systems using Swarm Learning
- Deliver accurate and trusted predictions of HumAIne systems leveraging domain knowledge based on Νeuro-Symbolic AI
- Benchmark Ηuman-ΑΙ models, algorithms and data
- Integrate the HumAIne platform and enable access through ergonomic and user-friendly human-machine interfaces
- Integrate HumAIne in the European AI ecosystem.
- Active Learning: facilitates the development of Human-in-the-Loop systems, involving humans in AI processes when faced with increased uncertainty.
- Neuro-Symbolic Learning: combines Deep Learning with semantics and rules, enabling the completion of complex tasks with high accuracy and minimal training data.
- Swarm Learning: fosters collaborative synergy through a networked swarm of AI agents, elevating collective intelligence for optimal system efficiency.
- Explainable AI (XAI): provides advanced models for predictive explanations, considering global context and allowing dynamic feedback from users.
The Project's Objectives may be summarized as follows:
- Define a standard-based Reference Architecture for collaborative HumAIne systems
- Ensure trustworthy, transparent and interoperable interactions between Ηumans and AI systems
- Accelerate knowledge acquisition based on Active Learning
- Enable collective intelligence for Humans and AI systems using Swarm Learning
- Deliver accurate and trusted predictions of HumAIne systems leveraging domain knowledge based on Νeuro-Symbolic AI
- Benchmark Ηuman-ΑΙ models, algorithms and data
- Integrate the HumAIne platform and enable access through ergonomic and user-friendly human-machine interfaces
- Integrate HumAIne in the European AI ecosystem.
HumAIne has initiated significant technical and scientific activities aimed at building a reliable and adaptable platform for Human-AI collaboration. A key milestone has been the specification of a comprehensive Reference Architecture for Human-AI Collaboration in Dynamic Environments, encompassing an ecosystem for integrating heterogeneous AI paradigms covering the AI lifecycle. This architecture is grounded in detailed functional and non-functional specifications and is being refined through ongoing alignment with real-world use cases.
To support transparency and trust in AI systems, the project has been working on the specification of a library of Explainable AI (XAI) techniques, a benchmarking suite for evaluating Human-AI models, and human-machine interfaces. These tools are designed to facilitate interaction between users and AI systems, supporting both technical and non-technical stakeholders.
Progress has also been made in defining the core AI platforms that underpin the HumAIne approach. These include platforms for Active Learning, Swarm Learning, and Neuro-Symbolic AI—each designed to enhance adaptability, collaboration, and knowledge integration in AI-driven decision-making.
In parallel, the project has outlined an Open Integration Platform for Human-AI collaboration and hybrid decision making, and developed training resources through a dedicated Learning Management System. These efforts aim to support the deployment and adoption of the platform by developers, researchers, and end-users.
Finally, we carried out a first validation of six pilots across diverse domains, which can integrate the different components that are being developed within the HumAIne platform. These pilots cover the sectors of: manufacturing, smart cities, healthcare, ticketing, and energy, and they are providing valuable insights into the platform’s applicability and user experience, helping to guide further development and refinement.
To support transparency and trust in AI systems, the project has been working on the specification of a library of Explainable AI (XAI) techniques, a benchmarking suite for evaluating Human-AI models, and human-machine interfaces. These tools are designed to facilitate interaction between users and AI systems, supporting both technical and non-technical stakeholders.
Progress has also been made in defining the core AI platforms that underpin the HumAIne approach. These include platforms for Active Learning, Swarm Learning, and Neuro-Symbolic AI—each designed to enhance adaptability, collaboration, and knowledge integration in AI-driven decision-making.
In parallel, the project has outlined an Open Integration Platform for Human-AI collaboration and hybrid decision making, and developed training resources through a dedicated Learning Management System. These efforts aim to support the deployment and adoption of the platform by developers, researchers, and end-users.
Finally, we carried out a first validation of six pilots across diverse domains, which can integrate the different components that are being developed within the HumAIne platform. These pilots cover the sectors of: manufacturing, smart cities, healthcare, ticketing, and energy, and they are providing valuable insights into the platform’s applicability and user experience, helping to guide further development and refinement.
HumAIne researches, develops, validates and promotes a novel platform for Human-AI collaboration. The project is pioneering a new era of human-AI collaboration by developing a unified platform that integrates cutting-edge AI technologies into a single, trusted system. This platform is built on a novel Reference Architecture that aims at ensuring ethical, human-centered AI development across sectors like healthcare, ticketing, manufacturing, energy, and smart cities. By combining advanced paradigms—such as Active Learning, Neuro-Symbolic AI, Swarm Learning, and Explainable AI—HumAIne empowers people to work alongside AI in dynamic, real-world environments. The result is smarter, more transparent decision-making systems where humans remain in control. With its emphasis on trust, adaptability, and inclusivity, HumAIne is set to transform how industries design and deploy AI, fostering a future where technology enhances—not replaces—human expertise. This project aims to establish itself as a benchmark for further development of competitive Artificial Intelligence within the European Union, and will build a vibrant community of interested parties around its complementary resources (e.g. training), to drive exploitation and wider use of the project's results.