EDBT 2026 Demo / reviewers in the wild / expert
Stefano Troiani
dblp:51/8366
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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 · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems › multi-robot coordination
multi-robot patrolling |
0.1 | 1 | 2010 | Moving game theoretical patrolling strategies from theory to practice: An USARSim simulation · ICRA 2010 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.1game theory · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Moving game theoretical patrolling strategies from theory to practice: An USARSim simulationabstractGame theoretical approaches have been recently used to develop patrolling strategies for mobile robots. The idea is that the patroller and the intruder play a game, whose outcome depends on the combination of their actions. From the analysis of this game, an optimal strategy for the patrolling robot can be derived. Although game theoretical approaches are promising, their applicability in real settings is still an open problem. In this paper, we experimentally evaluate the practical applicability of the most general game theoretical approach for patrolling strategies, called BGA model. Experiments are conducted by using USARSim, with the goal of studying the behavior of the optimal patrolling strategy returned by the BGA model both in situations that violate its idealized assumptions and in comparison with other patrolling strategies that can be developed with much less computational effort. Francesco Amigoni, Nicola Basilico, Nicola Gatti 0001, Alessandro Saporiti, Stefano Troiani |
ICRA | 5 |