Demo Data from EDBT 2026 / reviewers in the wild / expert
Marco Hanocq
dblp:430/7416
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 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 |
Knowledge representation and reasoning · 100% | |
| Theoretical computer science
1 paper |
Computational complexity · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
argumentation |
1.0 | 1 | 2026 | Hybrid Semantics Accounting for Argument Types · AAAI 2026 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › argumentation
gradual semantics |
1.0 | 1 | 2026 | Hybrid Semantics Accounting for Argument Types · AAAI 2026 |
Computational complexity › complexity of reasoning
complexity of argumentation semantics |
0.3 | 1 | 2026 | Hybrid Semantics Accounting for Argument Types · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
computational complexity analysis · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Hybrid Semantics Accounting for Argument TypesabstractAssessing the strength of arguments is essential for determining the outcomes of any argument-based system. A wide range of semantics has been proposed in the literature. These take as input a set of arguments—each assigned a basic weight and potentially subject to attacks from others—and compute a single strength value for each argument. Despite the diversity of argument types (or schemes), existing semantics apply uniform evaluation criteria across all arguments. In this paper, we advocate for type-dependent evaluations, acknowledging that the impact of attacks can vary across types. Given that many argument-based systems involve heterogeneous types of arguments, we propose a broad family of hybrid semantics that combine distinct base semantics, each tailored to specific argument types. We investigate their theoretical properties, present concrete instances within this family, and examine their computational complexity. Leila Amgoud, Marco Hanocq, Marie-Christine Lagasquie-Schiex |
AAAI | 2 |