Demo Data from EDBT 2026 / reviewers in the wild / expert
Natalie Abreu
dblp:324/1312
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2022
—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 |
Planning, search and constraint satisfaction · 87% Efficient and distributed learning · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › multi-agent path finding
learning-based multi-agent path finding |
0.6 | 1 | 2022 | Efficient Deep Learning for Multi Agent Pathfinding · AAAI 2022 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
multi-agent path finding |
0.6 | 1 | 2022 | Efficient Deep Learning for Multi Agent Pathfinding · AAAI 2022 |
Methods — techniques the papers use, named apart from their topics
supervised learning · 0.6reinforcement learning · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Efficient Deep Learning for Multi Agent PathfindingabstractMulti Agent Path Finding (MAPF) is widely needed to coordinate real-world robotic systems. New approaches turn to deep learning to solve MAPF instances, primarily using reinforcement learning, which has high computational costs. We propose a supervised learning approach to solve MAPF instances using a smaller, less costly model. Natalie Abreu |
AAAI | 1 |