Demo Data from SIGIR 2026 / reviewers in the wild / expert
Alejandra López de Aberasturi-Gómez
dblp:356/4631
· DBLP profile ↗
3ranked-venue papers
3as first author
3since 2021 · last 2025
0000-0001-6895-2217ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-author · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
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.
| Theoretical computer science
2 papers |
Algorithmic game theory and mechanism design · 100% | |
| Artificial intelligence
2 papers |
Multi-agent systems · 77% Reinforcement learning · 23% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems › multi-agent collaboration
teamwork |
1.7 | 2 | 2025 | Grounded predictions of teamwork as a one-shot game: A multiagent multi-armed bandits approach · Artif. Intell. 2025 A Semantic Framework for Neurosymbolic Computation (Abstract Reprint) · IJCAI 2025 |
Algorithmic game theory and mechanism design › non-cooperative game › strategic game
aggregative games |
1.7 | 2 | 2025 | Grounded predictions of teamwork as a one-shot game: A multiagent multi-armed bandits approach · Artif. Intell. 2025 A Semantic Framework for Neurosymbolic Computation (Abstract Reprint) · IJCAI 2025 |
Algorithmic game theory and mechanism design › solution concepts in games › equilibrium concepts
nash equilibrium |
1.7 | 2 | 2025 | Grounded predictions of teamwork as a one-shot game: A multiagent multi-armed bandits approach · Artif. Intell. 2025 A Semantic Framework for Neurosymbolic Computation (Abstract Reprint) · IJCAI 2025 |
Machine learning › Reinforcement learning › multi-armed bandit
multi-agent bandit |
0.3 | 1 | 2025 | A Semantic Framework for Neurosymbolic Computation (Abstract Reprint) · IJCAI 2025 |
Machine learning › Reinforcement learning
multi-armed bandit |
0.3 | 1 | 2025 | A Semantic Framework for Neurosymbolic Computation (Abstract Reprint) · IJCAI 2025 |
Methods — techniques the papers use, named apart from their topics
neurosymbolic computation · 1.7multi-armed bandit · 1.7game theory · 1.7multiagent multi-armed bandit · 0.9multi-agent multi-armed bandit · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | A Semantic Framework for Neurosymbolic Computation (Abstract Reprint)abstractHumans possess innate collaborative capacities. However, effective teamwork often remains challenging. This study delves into the feasibility of collaboration within teams of rational, selfinterested agents who engage in teamwork without the obligation to contribute. Drawing from psychological and game theoretical frameworks, we formalise teamwork as a one-shot aggregative game, integrating insights from Steiner’s theory of group productivity. We characterise this novel game’s Nash equilibria and propose a multiagent multi-armed bandit system that learns to converge to approximations of such equilibria. Our research contributes value to the areas of game theory and multiagent systems, paving the way for a better understanding of voluntary collaborative dynamics. We examine how team heterogeneity, task typology, and assessment difficulty inuence agents’ strategies and resulting teamwork outcomes. Finally, we empirically study the behaviour of work teams under incentive systems that defy analytical treatment. Our agents demonstrate human-like behaviour patterns, corroborating ndings from social psychology research. Alejandra López de Aberasturi-Gómez, Jordi Sabater-Mir, Carles Sierra |
IJCAI | 1 |
| 2025 | Grounded predictions of teamwork as a one-shot game: A multiagent multi-armed bandits approachabstractHumans possess innate collaborative capacities. However, effective teamwork often remains challenging. This study delves into the feasibility of collaboration within teams of rational, self-interested agents who engage in teamwork without the obligation to contribute. Drawing from psychological and game theoretical frameworks, we formalise teamwork as a one-shot aggregative game, integrating insights from Steiner's theory of group productivity. We characterise this novel game's Nash equilibria and propose a multiagent multi-armed bandit system that learns to converge to approximations of such equilibria. Our research contributes value to the areas of game theory and multiagent systems, paving the way for a better understanding of voluntary collaborative dynamics. We examine how team heterogeneity, task typology, and assessment difficulty influence agents' strategies and resulting teamwork outcomes. Finally, we empirically study the behaviour of work teams under incentive systems that defy analytical treatment. Our agents demonstrate human-like behaviour patterns, corroborating findings from social psychology research. Alejandra López de Aberasturi-Gómez, Carles Sierra, Jordi Sabater-Mir |
Artif. Intell. | 1 |
| 2023 | Modelling Group Performance in Multiagent Systems: Introducing the CollabQuest Simulation Game
Alejandra López de Aberasturi-Gómez, Jordi Sabater-Mir, Carles Sierra |
EUMAS | 1 |