EDBT 2026 Demo / reviewers in the wild / expert
Mona Henle
dblp:309/5706
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 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.
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › solution concepts in games › equilibrium concepts
approximate equilibrium |
0.6 | 1 | 2022 | Multi-Leader Congestion Games with an Adversary · AAAI 2022 |
Algorithmic game theory and mechanism design
congestion games |
0.6 | 1 | 2022 | Multi-Leader Congestion Games with an Adversary · AAAI 2022 |
Algorithmic game theory and mechanism design › solution concepts in games › equilibrium concepts
nash equilibrium |
0.6 | 1 | 2022 | Multi-Leader Congestion Games with an Adversary · AAAI 2022 |
Methods — techniques the papers use, named apart from their topics
combinatorial algorithms · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Multi-Leader Congestion Games with an Adversary
Tobias Harks, Mona Henle, Max Klimm, Jannik Matuschke, Anja Schedel |
J. Artif. Intell. Res. | 2 |
| 2022 | Multi-Leader Congestion Games with an AdversaryabstractWe study a multi-leader single-follower congestion game where multiple users (leaders) choose one resource out of a set of resources and, after observing the realized loads, an adversary (single-follower) attacks the resources with maximum loads causing additional costs for the leaders. For the resulting strategic game among the leaders, we show that pure Nash equilibria fail to exist and therefore, we consider approximate equilibria instead. As our first main result, we show that the existence of a K-approximate equilibrium can always be guaranteed, where K (approximately equal to 1.1974) is the unique solution of a cubic polynomial equation. To this end, we give a polynomial time combinatorial algorithm which computes a K-approximate equilibrium. The factor K is tight, meaning that there is an instance that does not admit an A-approximate equilibrium for any A < K. Thus A = K is the smallest possible value of A such that the existence of an A-approximate equilibrium can be guaranteed for any instance of the considered game. Secondly, we focus on approximate equilibria of a given fixed instance. We show how to compute efficiently a best approximate equilibrium, that is, with smallest possible A among all A-approximate equilibria of the given instance. Tobias Harks, Mona Henle, Max Klimm, Jannik Matuschke, Anja Schedel |
AAAI | 2 |