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Alejandra López de Aberasturi-Gómez

dblp:356/4631 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems › multi-agent collaboration
teamwork
1.722025
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
1.722025
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
1.722025
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
0.312025
A Semantic Framework for Neurosymbolic Computation (Abstract Reprint) · IJCAI 2025
Machine learning › Reinforcement learning
multi-armed bandit
0.312025
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
YearPublicationVenuePosition
2025 A Semantic Framework for Neurosymbolic Computation (Abstract Reprint)
abstract
Humans 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
IJCAI1
2025 Grounded predictions of teamwork as a one-shot game: A multiagent multi-armed bandits approach
abstract
Humans 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
EUMAS1