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CAI4DSA: Collaborative Explainable neuro-symbolic AI for Decision Support Assistant, FAIR PE1 Spoke 1, PNRR

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CAI4DSA: Collaborative Explainable neuro-symbolic AI for Decision Support Assistant

Objective #1: Novel methods for dynamic, collaborative human-AI systems

University of Florence, UNIFI, DINFO department

 A VALERE SULLE RISORSE DEL PIANO NAZIONALE RIPRESA E RESILIENZA (PNRR) MISSIONE 4, “ISTRUZIONE E RICERCA” - COMPONENTE 2, “DALLA RICERCA ALL’IMPRESA” - LINEA DI INVESTIMENTO 1.3, FINANZIATO DALL’UNIONE EUROPEA – NEXTGENERATIONEU”, PROGETTO “FUTURE ARTIFICIAL INTELLIGENCE RESEARCH – FAIR” PE0000013, CUP I53C22001380006. FAIR: https://fondazione-fair.it/en/

CAI4DSA aims at exploiting AI for creating decision support systems (DSS) which can evolve with the collaboration among and with humans. The NGDSS should be capable to react to changes in the context, interact with humans, and learn from the occurrence. Symbolic and neural models could be also shared in communities to create a global knowledge and understanding of the problems and solutions. The main objectives will be to develop new knowledge and solutions regarding: Continuous incremental reinforced learning; human readable multidomain XAI representations; extract and formalize new knowledge from XAI, results/KPIs as facts, relationships, constraints, models (causals, maths, etc.); human NGDSS interaction which should be capable to work reconcile with humans; and validation of NGDSS in a number of critical infrastructure domains.  
In this context, a significant relevance is attributed to: continuous incremental reinforced learning; human readable multidomain XAI representations to facilitate human understanding and trust; extract and formalize new knowledge from XAI, results/KPIs; human NGDSS interaction;  validation of NGDSS in critical infrastructure domains.  The research will be based on an iterative approach that focuses on developing, evaluating, and deploying artifacts that address specific problems or opportunities in the specific context. The iterative nature would allow for continuous improvement of the NGDSS based on feedback from decision makers in the target domain. A number of reference cases in different domains will be used for validation for which details and data are accessible: mobility and transport, medicine, complex manufacturing, and security. The innovative outcomes of this research project will be:
•    New Algorithms or Models: Development of novel AI algorithms or models that can enhance decision-making processes.
•    NGDSS Theories: Contribution to the theoretical understanding of how AI can be effectively integrated into decision support systems.
•    Early Prototype of the NGDSS, providing a proof of concept for how AI can enhance decision-making in a number of target domains
The project results will be sustainable since the results will be proof with respect to real cases. This approach will also guarantee a strong impact in terms of: new knowledge on neuro-symbolic DSS,  research publications and presentations, enlargement of research network,  ethical and societal implications due to the domain of validation,  future research directions, and finally also strong possibilities of the exploitation of results via some technology transfer and commercialization of results.

Keywords (Main project keywords):  decision support systems, XAI/AI, KPI-based XAI, neuro-symbolic approach, human in the loop.

started: 01-08-2024

Duration: 15 months

Coordinator: Paolo Nesi, paolo.nesi@unifi.it

 

 

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CAI4DSA: Collaborative Explainable neuro-symbolic AI for Decision Support Assistant, FAIR PE1 Spoke 1, PNRR | Snap4City

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