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Ramon Sangüesa

dblp:s/RamonSanguesa · also Ramón Sangüesa · DBLP profile ↗
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12ranked-venue papers
6as first author
0since 2021 · last 2008
0000-0002-0998-4179ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 10 · 6 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Multi-agent systems · 72% Probabilistic and Bayesian machine learning · 24% Knowledge representation and reasoning · 4%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning
clustering
0.112005
The Role of Clustering on the Emergence of Efficient Social Conventions · IJCAI 2005
Knowledge, reasoning and agents › Multi-agent systems › normative multi-agent systems › social norms
convention formation
0.112005
The Role of Clustering on the Emergence of Efficient Social Conventions · IJCAI 2005
Knowledge, reasoning and agents › Multi-agent systems
multi-agent coordination
0.112005
The Role of Clustering on the Emergence of Efficient Social Conventions · IJCAI 2005
Knowledge, reasoning and agents › Multi-agent systems › normative multi-agent systems
social norms
0.112005
The Role of Clustering on the Emergence of Efficient Social Conventions · IJCAI 2005
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge acquisition
0.011991
Knowledge Acquisition Combining Analytical and Empirrcal Techniques · ML 1991

Methods — techniques the papers use, named apart from their topics

empirical techniques · 0.0analytical techniques · 0.0
YearPublicationVenuePosition
2008 Optimizing Decentralized Grid Markets through Group Selection
abstract
Automatic coordination mechanisms for the Grid are required due to the increasing complexity exhibited in large scale distributed systems. Decentralized economic models are being considered as scalable coordination mechanisms for the management of service allocations to clients. However, decentralization incorporates further dynamicity and unpredictability into the system. Introducing higher levels of adaptation and learning in the coordination protocols helps cope with complexity. We provide a solution based on a self-organized, emergent mechanism evolving Grid Market participants through a group selection process. Dynamic congregations organize agents into optimized market segments, maximizing utility and thereby improving system-wide performance. We provide a system model and evaluation by simulation of the group selection mechanism. We further provide a prototype showing practical feasibility of the approach.
Isaac Chao, Oscar Ardaiz-Villanueva, Ramon Sangüesa, Liviu Joita, Omer F. Rana
CISIS3
2006 Video transcoding in a Grid network with User Controlled LightPaths
Eduard Grasa, Sergi Figuerola, Joaquim Recio, Albert López, Marc de Palol, Lluis Ribes, Vicente Díaz, Ramon Sangüesa, Gabriel Junyent, Michel Savoie
Future Gener. Comput. Syst.8
2005 The Role of Clustering on the Emergence of Efficient Social Conventions
Josep M. Pujol, Jordi Delgado, Ramon Sangüesa, Andreas Flache
IJCAI3
2004 Emergence of Complex Networks through Local Optimization
Josep M. Pujol, Andreas Flache, Ramon Sangüesa, Jordi Delgado
ECAI3
2003 Emergence of coordination in scale-free networks
Jordi Delgado, Josep M. Pujol, Ramon Sangüesa
Web Intell. Agent Syst.3
2001 Editorial: Special Issue on causal networks: papers from the Second CaNew Workshop
Ramon Sangüesa, Ulises Cortés
Int. J. Approx. Reason.1
2000 Application of Bayesian Network Learning Methods to Waste Water Treatment Plants
Ramon Sangüesa, Phillip Burrell
Appl. Intell.1
2000 New perspectives on Causal Networks: the first CaNew workshop
Ramon Sangüesa, Ulises Cortés
Int. J. Approx. Reason.1
2000 Prior knowledge for learning networks in non-probabilistic settings
Ramon Sangüesa, Ulises Cortés
Int. J. Approx. Reason.1
1998 Possibilistic conditional independence: A similarity-based measure and its application to causal network learning
Ramon Sangüesa, Joan Cabós, Ulises Cortés
Int. J. Approx. Reason.1
1998 A parallel algorithm for building possibilistic causal networks
Ramon Sangüesa, Ulises Cortés, Antonio Gisolfi
Int. J. Approx. Reason.1
1991 Knowledge Acquisition Combining Analytical and Empirrcal Techniques
Mario Martín, Ramon Sangüesa, Ulises Cortés
ML2