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Browsing Congressos (Departament de Tecnologies de la Informació i les Comunicacions) by Author "Furelos Blanco, Daniel"

Browsing Congressos (Departament de Tecnologies de la Informació i les Comunicacions) by Author "Furelos Blanco, Daniel"

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  • Furelos Blanco, Daniel; Bucchiarone, Antonio; Jonsson, Anders, 1973- (International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2018)
    In this paper we present the CARPooL demonstrator, an implementation of a Collective Adaptation Engine (CAE) that addresses the challenge of collective adaptation in the smart mobility domain. CARPooL resolves adaptation ...
  • Bucchiarone, Antonio; Furelos Blanco, Daniel; Jonsson, Anders, 1973-; Khandokar, Fahmida; Mourshed, Monjur (International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2018)
    In this paper we address the challenges that impede collective adaptation in smart mobility systems by proposing a notion of ensembles. Ensembles enable systems with collective adaptability to be built as emergent aggregations ...
  • Furelos Blanco, Daniel; Jonsson, Anders, 1973-; Palacios Verdes, Héctor Luis; Jiménez, Sergio (Association for the Advancement of Artificial Intelligence (AAAI) - Congrés ICAPS17, 2018)
    In this paper we describe STP, a novel algorithm for temporal planning. Similar to several existing temporal planners, STP relies on a transformation from temporal planning to classical planning, and constructs a temporal ...
  • Furelos Blanco, Daniel; Jonsson, Anders, 1973- (Association for the Advancement of Artificial Intelligence (AAAI) - Congrés ICAPS17, 2018)
    In this work we present a novel approach to solving concurrent multiagent planning problems in which several agents act in parallel. Our approach relies on a compilation from concurrent multiagent planning to classical ...
  • Furelos Blanco, Daniel; Jonsson, Anders, 1973- (Association for the Advancement of Artificial Intelligence (AAAI), 2019)
    In this work we present a novel approach to solving concurrent multiagent planning problems in which several agents act in parallel. Our approach relies on a compilation from concurrent multiagent planning to classical ...

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