19-23 September 2022
Prague, Czech Republic
Europe/Amsterdam timezone

The AI4PublicPolicy Virtualized Policy Management Environment (VPME) with fully-fledged policy development/management functionalities based on AI technologies

20 Sep 2022, 19:00
1st floor (Vienna Andel Prague)

1st floor

Vienna Andel Prague

Poster Machine Learning/Artificial Intelligence Posters (presenters at poster)


Alessandro Amicone (GFT Italy)


AI4PublicPolicy is a joint effort of policymakers and Cloud/AI experts to unveil AI’s potential for automated, transparent and citizen-centric development of public policies. To this end, the project will deliver a novel Open Cloud platform for automated, scalable, transparent and citizen-centric policy management. The AI4PublicPolicy platform, i.e. the Virtualized Policy Management Environment (VPME) will provide fully-fledged policy development/management functionalities based on AI technologies such as Machine Learning, Deep Learning, NLP and chatbots, while leveraging citizens’ participation and feedback.
More specifically, within the framework of the VPME, the following components are being developed:
• Data Management Toolkit (UPM)
• Policy & Dataset Catalogue (UNP)
• Policy Extraction Toolkit (GFT)
• Policy Interpretation Toolkit (INTRA)
• Interoperability Toolkit (UNP)
• Policy Evaluation Toolkit (GFT)
The abovementioned technologies will be deployed in the scope of the five real life pilots of the project, i.e.:
• Athens, Greece: Policies for Infrastructures Maintenance and Repair, Parking Space Management and Urban Mobility
• Genoa, Italy: Policies for Citizens and Business Services Optimization
• Nicosia, Cyprus: Policies for Holistic Urban Mobility and Accessibility
• Lisbon, Portugal: Energy Management and Optimization Policies
• Burgas, Bulgaria: Data-Driven Water Infrastructure Planning and Maintenance Policies
The VPME will be integrated with EOSC in order to facilitate access to the Cloud and HPC resources of EOSC/EGI that are required to enable the project’s AI tools, and second to boost the sustainability and wider use of the project’s developments.

Any relevant links


Topic Machine Learning/Artificial Intelligence

Primary authors

Ada Pellumbaj (ViLabs) Alessandro Amicone (GFT Italy)

Presentation Materials

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