Research output per year
Research output per year
Project Details
Description
In this proposal we focus on challenges regarding infectious diseases such as mitigating the COVID-19 pandemic, measles elimination and optimal influenza vaccination and draw motivation from fundamental questions with respect to health-care prioritisation. Advancements in the fields of epidemic modelling, health economics and multi-agent learning paired with formally-verified guarantees can improve the effectiveness and reliability of the complex decision-making mechanisms needed to answer such questions.
This complexity is situated in different aspects: the computational complexity, model structure and the interactions between multiple agents, in combination with multiple objectives to optimise. Our consortium establishes a unique combination of leading expertise, which as a team not only will advance each of these fields, but also develops a new, internationally unique, multidisciplinary research line.
This complexity is situated in different aspects: the computational complexity, model structure and the interactions between multiple agents, in combination with multiple objectives to optimise. Our consortium establishes a unique combination of leading expertise, which as a team not only will advance each of these fields, but also develops a new, internationally unique, multidisciplinary research line.
| Acronym | iBOF/21/027 |
|---|---|
| Status | Active |
| Effective start/end date | 1/01/21 → 31/12/26 |
Keywords
- Artificial intelligence
- Mathematical modelling
- Health economics
- Formal verification
- infectious disease
Flemish discipline codes in use since 2023
- Machine learning and decision making
- Epidemiology
- Health, education and welfare economics
- Computational logic and formal languages
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Composing Reinforcement Learning Policies, with Formal Guarantees
Delgrange, F., Avni, G., Lukina, A., Schilling, C., Nowe, A. & Pérez, G., 5 Jun 2025, Composing Reinforcement Learning Policies, with Formal Guarantees. Detroit, MI, USA: ACM, Vol. Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems. p. 574-583 10 p.Research output: Chapter in Book/Report/Conference proceeding › Conference paper
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Controller Synthesis from Deep Reinforcement Learning Policies
Delgrange, F., Avni, G., Lukina, A., Schilling, C., Nowe, A. & Pérez, G. A., 4 Mar 2025. 31 p.Research output: Unpublished contribution to conference › Unpublished paper
Open Access -
Integrating RL and Planning through Optimal Transport World Models
Röpke, W., Avalos, R., Radulescu, R., Nowe, A., Roijers, D. M. & Delgrange, F., 19 May 2025. 14 p.Research output: Unpublished contribution to conference › Unpublished paper
Open Access
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Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees
Florent Delgrange (Speaker), Ann Nowe (Contributor) & Guillermo A. Pérez (Contributor)
7 Nov 2022 → 9 Nov 2022Activity: Talk or presentation › Talk or presentation at a conference
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36th AAAI conference on artificial intelligence
Florent Delgrange (Participant)
22 Feb 2022 → 1 Mar 2022Activity: Participating in or organising an event › Participation in conference