Zobrazeno 1 - 10
of 13 654
pro vyhledávání: '"A. Mannion"'
This study investigates the emergence of collective identity among individuals critical of vaccination policies in France during the COVID-19 pandemic. As concerns grew over mandated health measures, a loose collective formed on Twitter to assert aut
Externí odkaz:
http://arxiv.org/abs/2410.12676
Publikováno v:
Social Network Analysis and Mining 14, 199 (2024)
In this article, we propose and apply a method to compare adaptations of the same story across different media. We tackle this task by modelling such adaptations through character networks. We compare them by leveraging two concepts at the core of st
Externí odkaz:
http://arxiv.org/abs/2410.05453
Many decision-making problems feature multiple objectives where it is not always possible to know the preferences of a human or agent decision-maker for different objectives. However, demonstrated behaviors from the decision-maker are often available
Externí odkaz:
http://arxiv.org/abs/2409.20258
Addressing the question of how to achieve optimal decision-making under risk and uncertainty is crucial for enhancing the capabilities of artificial agents that collaborate with or support humans. In this work, we address this question in the context
Externí odkaz:
http://arxiv.org/abs/2408.00682
Autor:
Felten, Florian, Ucak, Umut, Azmani, Hicham, Peng, Gao, Röpke, Willem, Baier, Hendrik, Mannion, Patrick, Roijers, Diederik M., Terry, Jordan K., Talbi, El-Ghazali, Danoy, Grégoire, Nowé, Ann, Rădulescu, Roxana
Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting objectives and coordinate the actions of various independent decision-makers
Externí odkaz:
http://arxiv.org/abs/2407.16312
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments
Effective residential appliance scheduling is crucial for sustainable living. While multi-objective reinforcement learning (MORL) has proven effective in balancing user preferences in appliance scheduling, traditional MORL struggles with limited data
Externí odkaz:
http://arxiv.org/abs/2407.11489
Autor:
Mehonic, Adnan, Ielmini, Daniele, Roy, Kaushik, Mutlu, Onur, Kvatinsky, Shahar, Serrano-Gotarredona, Teresa, Linares-Barranco, Bernabe, Spiga, Sabina, Savelev, Sergey, Balanov, Alexander G, Chawla, Nitin, Desoli, Giuseppe, Malavena, Gerardo, Compagnoni, Christian Monzio, Wang, Zhongrui, Yang, J Joshua, Syed, Ghazi Sarwat, Sebastian, Abu, Mikolajick, Thomas, Noheda, Beatriz, Slesazeck, Stefan, Dieny, Bernard, Tuo-Hung, Hou, Varri, Akhil, Bruckerhoff-Pluckelmann, Frank, Pernice, Wolfram, Zhang, Xixiang, Pazos, Sebastian, Lanza, Mario, Wiefels, Stefan, Dittmann, Regina, Ng, Wing H, Buckwell, Mark, Cox, Horatio RJ, Mannion, Daniel J, Kenyon, Anthony J, Lu, Yingming, Yang, Yuchao, Querlioz, Damien, Hutin, Louis, Vianello, Elisa, Chowdhury, Sayeed Shafayet, Mannocci, Piergiulio, Cai, Yimao, Sun, Zhong, Pedretti, Giacomo, Strachan, John Paul, Strukov, Dmitri, Gallo, Manuel Le, Ambrogio, Stefano, Valov, Ilia, Waser, Rainer
The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, add
Externí odkaz:
http://arxiv.org/abs/2407.02353
Autor:
I. Caelers, A. Mannion, D. Haschtmann, K. Rijkers, W. Van Hemert, R. De Bie, H. Van Santbrink
Publikováno v:
Brain and Spine, Vol 2, Iss , Pp 101218- (2022)
Externí odkaz:
https://doaj.org/article/a5904392d1cb49628961dd24157ffc69
Multi-objective reinforcement learning (MORL) is increasingly relevant due to its resemblance to real-world scenarios requiring trade-offs between multiple objectives. Catering to diverse user preferences, traditional reinforcement learning faces amp
Externí odkaz:
http://arxiv.org/abs/2404.03997
Autor:
Röpke, Willem, Reymond, Mathieu, Mannion, Patrick, Roijers, Diederik M., Nowé, Ann, Rădulescu, Roxana
A significant challenge in multi-objective reinforcement learning is obtaining a Pareto front of policies that attain optimal performance under different preferences. We introduce Iterated Pareto Referent Optimisation (IPRO), a principled algorithm t
Externí odkaz:
http://arxiv.org/abs/2402.07182