Items where Author is "Serramia, M."
    
      Abia Alonso, V., Serramia, M. & Alonso, E. 
ORCID: 0000-0002-3306-695X  (2025).
      Finding Our Moral Values: Guidelines for Value System Aggregation.
      Paper presented at the The 6th International Workshop on Democracy & AI, IJCAI25, 16-22 Aug 2025, Montreal, Canada.
    
    
    
  
Serramia, M., Lopez-Sanchez, M., Moretti, S. & Rodriguez-Aguilar, J. A. (2023). Building rankings encompassing multiple criteria to support qualitative decision-making. Information Sciences, 631, pp. 288-304. doi: 10.1016/j.ins.2023.02.063
    Aydoğan, R., Criado, N., Lang, J. , Sanchez-Anguix, V. & Serramia, M. 
ORCID: 0000-0003-0993-024X (2022).
    Preface.
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13753 LNAI,
    
    
     pp. 20-36.
    doi: 10.1007/978-3-031-21203-1_2
  
    
    
      
        
        Lera-Leri, R., Bistaffa, F., Serramia, M. 
ORCID: 0000-0003-0993-024X , Lopez-Sanchez, M. & Rodriguez-Aguilar, J. A. (2022).
        
        Towards Pluralistic Value Alignment: Aggregating Value Systems through ℓp-Regression.
        
        In: 
        
        
        AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems.
        
        
        AAMAS '22: 21st International Conference on Autonomous Agents and Multiagent Systems, 9-13 May 2022, Online.
      
      
    
    
  
    Rodriguez-Soto, M., Serramia, M. 
ORCID: 0000-0003-0993-024X, Lopez-Sanchez, M.  & Antonio Rodriguez-Aguilar, J. (2022).
    Instilling moral value alignment by means of multi-objective reinforcement learning.
    Ethics and Information Technology, 24(1),
    
    article number 9.
    
    doi: 10.1007/s10676-022-09635-0
  
    Serramia, M. 
ORCID: 0000-0003-0993-024X, Lopez-Sanchez, M., Moretti, S.  & Rodriguez-Aguilar, J. A. (2021).
    On the dominant set selection problem and its application to value alignment.
    Autonomous Agents and Multi-Agent Systems, 35(2),
    
    article number 42.
    
    doi: 10.1007/s10458-021-09519-5
  
              
              
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