Search Results for author: Juan A. Rodríguez-Aguilar

Found 6 papers, 1 papers with code

Predicting Requests in Large-Scale Online P2P Ridesharing

no code implementations7 Sep 2020 Filippo Bistaffa, Juan A. Rodríguez-Aguilar, Jesús Cerquides

Peer-to-peer ridesharing (P2P-RS) enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers.

TAIP: an anytime algorithm for allocating student teams to internship programs

no code implementations19 May 2020 Athina Georgara, Carles Sierra, Juan A. Rodríguez-Aguilar

In scenarios that require teamwork, we usually have at hand a variety of specific tasks, for which we need to form a team in order to carry out each one.

Synergistic Team Composition: A Computational Approach to Foster Diversity in Teams

no code implementations26 Sep 2019 Ewa Andrejczuk, Filippo Bistaffa, Christian Blum, Juan A. Rodríguez-Aguilar, Carles Sierra

Thus, the goal of the STCP is to partition a set of individuals into a set of synergistic teams: teams that are diverse in personality and gender and whose members cover all required competencies to complete a task.

Improving Max-Sum through Decimation to Solve Loopy Distributed Constraint Optimization Problems

no code implementations7 Jun 2017 Jesús Cerquides, Rémi Emonet, Gauthier Picard, Juan A. Rodríguez-Aguilar

In the context of solving large distributed constraint optimization problems (DCOP), belief-propagation and approximate inference algorithms are candidates of choice.

Algorithms for Graph-Constrained Coalition Formation in the Real World

1 code implementation13 Dec 2016 Filippo Bistaffa, Alessandro Farinelli, Jesús Cerquides, Juan A. Rodríguez-Aguilar, Sarvapali D. Ramchurn

In this paper, we focus on a special case of coalition formation known as Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the agents constrains the formation of coalitions.

Worst-case bounds on the quality of max-product fixed-points

no code implementations NeurIPS 2010 Meritxell Vinyals, Jes\'Us Cerquides, Alessandro Farinelli, Juan A. Rodríguez-Aguilar

We study worst-case bounds on the quality of any fixed point assignment of the max-product algorithm for Markov Random Fields (MRF).

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