EDBT 2026 Demo / reviewers in the wild / expert
Alexandra Neacsu
dblp:324/6104
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Planning, search and constraint satisfaction · 100% |
Topics — the 3 heaviest of 4, 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 planning
cooperative multi-agent planning |
0.8 | 1 | 2024 | Optimizing pathfinding for goal legibility and recognition in cooperative partially observable environments · Artif. Intell. 2024 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › plan recognition
goal recognition |
0.8 | 1 | 2024 | Optimizing pathfinding for goal legibility and recognition in cooperative partially observable environments · Artif. Intell. 2024 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
multi-agent path finding |
0.8 | 1 | 2024 | Optimizing pathfinding for goal legibility and recognition in cooperative partially observable environments · Artif. Intell. 2024 |
Methods — techniques the papers use, named apart from their topics
minimum cost flow · 0.8goal recognition mapping · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Optimizing pathfinding for goal legibility and recognition in cooperative partially observable environmentsabstractIn this paper, we perform a joint design of goal legibility and recognition in a cooperative, multi-agent pathfinding setting with partial observability. More specifically, we consider a set of identical agents (the actors) that move in an environment only partially observable to an observer in the loop. The actors are tasked with reaching a set of locations that need to be serviced in a timely fashion. The observer monitors the actors' behavior from a distance and needs to identify each actor's destination based on the actor's observable movements. Our approach generates legible paths for the actors; namely, it constructs one path from the origin to each destination so that these paths overlap as little as possible while satisfying budget constraints. It also equips the observer with a goal-recognition mapping between unique sequences of observations and destinations, ensuring that the observer can infer an actor's destination by making the minimum number of observations (legibility delay). Our method substantially extends previous work, which is limited to an observer with full observability, showing that optimizing pathfinding for goal legibility and recognition can be performed via a reformulation into a classical minimum cost flow problem in the partially observable case when the algorithms for the fully observable case are appropriately modified. Our empirical evaluation shows that our techniques are as effective in partially observable settings as in fully observable ones. Sara Bernardini, Fabio Fagnani, Alexandra Neacsu, Santiago Franco |
Artif. Intell. | 3 |