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Collectiveware: Highly-parallel algorithms for collective intelligence

Total Cost €


EC-Contrib. €






Project "HPA4CF" data sheet

The following table provides information about the project.


Organization address
address: CALLE SERRANO 117
city: MADRID
postcode: 28006

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Spain [ES]
 Project website
 Total cost 158˙121 €
 EC max contribution 158˙121 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2017
 Duration (year-month-day) from 2017-06-16   to  2019-06-15


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

In recent years, more and more scenarios pose challenges that require collective intelligence solutions based on networks (knowledge networks, social networks, sensor networks). New forms of collaborative consumption, collaborative making, collaborative production, all rely on a common task, the formation of collectives. This task is crucial in many real-world applications domains. Notable examples of actual-world collective formation scenarios are Collective Energy Purchasing (CEP), a collaborative consumption scenario, and Team Formation (TF), a collaborative production scenario. Within the Artificial Intelligence literature, current state of the art algorithms cannot provide the level of scalability and the solution quality required by actual-world collective formation problems, hence novel algorithms are needed to tackle these problems. To achieve this objective, we aim at proposing novel algorithms that are capable to exploit modern highly-parallel architectures. On the one hand, highly-parallel architectures have been successfully applied in many different scenarios so to achieve tremendous performance improvements. These advancements encourage the investigation of parallelisation also in collective formation, with the objective of achieving the same benefits. On the other hand, our past research indicates that considering the structure of the collective formation problem leads to notable benefits in terms of scalability and solution quality. Thus, we propose to take a novel algorithmic design approach that considers both the structure of the scenario and at the same time exploits modern highly-parallel architectures. Our algorithms will be evaluated in two prominent collective intelligence application domains: the CEP and TF domains. The choice of these two application domains will serve to show the generality of our algorithmic design approach, since they are representative of two structurally different families of actual-world collective formation problems.


year authors and title journal last update
List of publications.
2019 Ewa Andrejczuk, Filippo Bistaffa, Christian Blum, Juan A. Rodríguez-Aguilar, and Carles Sierra
Synergistic team composition: A computational approach to foster diversity in teams
published pages: , ISSN: 0950-7051, DOI: 10.1016/j.knosys.2019.06.007
Knowledge-Based Systems 2020-01-21
2018 Filippo Bistaffa, Alessandro Farinelli
A COP Model For Graph-Constrained Coalition Formation
published pages: 133-153, ISSN: 1076-9757, DOI: 10.1613/jair.1.11205
Journal of Artificial Intelligence Research 62 2020-01-21
2018 Ewa Andrejczuk, Filippo Bistaffa, Christian Blum, Juan A. Rodr´ıguez-Aguilar, Carles Sierra
Solving the Synergistic Team Composition Problem
published pages: 1853-1855, ISSN: , DOI:
AAMAS \'18 Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems 2020-01-21
2018 Filippo Bistaffa, Juan Rodríguez-Aguilar, Jesús Cerquides, Christian Blum
A Simulation Tool for Large-Scale Online Ridesharing
published pages: 1797-1799, ISSN: , DOI:
AAMAS \'18 Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems 2020-01-21
2017 Filippo Bistaffa, Alessandro Farinelli, Georgios Chalkiadakis, Sarvapali D. Ramchurn
A cooperative game-theoretic approach to the social ridesharing problem
published pages: 86-117, ISSN: 0004-3702, DOI: 10.1016/j.artint.2017.02.004
Artificial Intelligence 246 2020-01-21

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