Demo Data from EDBT 2026 / reviewers in the wild / expert
Thalya Fossey
dblp:415/3493
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 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.
| Artificial intelligence
1 paper |
Multi-agent systems · 75% Knowledge representation and reasoning · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
argumentation |
0.9 | 1 | 2025 | Argument-based Multi-Issue Negotiation · IJCAI 2025 |
Knowledge, reasoning and agents › Multi-agent systems › automated negotiation
argument-based negotiation |
0.9 | 1 | 2025 | Argument-based Multi-Issue Negotiation · IJCAI 2025 |
Knowledge, reasoning and agents › Multi-agent systems
automated negotiation |
0.9 | 1 | 2025 | Argument-based Multi-Issue Negotiation · IJCAI 2025 |
Knowledge, reasoning and agents › Multi-agent systems › automated negotiation
multi-issue negotiation |
0.9 | 1 | 2025 | Argument-based Multi-Issue Negotiation · IJCAI 2025 |
Methods — techniques the papers use, named apart from their topics
utility modeling · 0.9concession mechanism · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Argument-based Multi-Issue NegotiationabstractAutomated negotiation aims at finding agreements between agents with conflicting goals. Existing utility-based approaches guarantee agents satisfaction with negotiation outcomes, especially in multi-issue negotiations where concession mechanisms lead to win-win results. However, they lack explainability and do not consider agents’ beliefs. On the other hand, argument-based approaches provide reasons for accepting or rejecting offers but do not include utility modeling for offers or enable concession mechanisms in multi-issue settings. We propose a novel hybrid approach combining both types of approaches. The utility-based component enables agents to make concessions on complex negotiation objects to achieve win-win outcomes, while the argumentation component ensures that accepted offers align with the agents' personal argumentation theories. These theories represent their beliefs, encoding various profiles, ethical considerations, social norms, or legal principles. Thalya Fossey, Jean-Guy Mailly, Pavlos Moraitis |
IJCAI | 1 |