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Emile Siboulet

dblp:442/1118 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
—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 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 · 50% Multi-agent systems · 50%
Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
multi-agent planning
0.912025
A Flow Based Planning Method for Multi-Agent Progression with Deployable Agents and Communication Constraints · ICAPS 2025
Knowledge, reasoning and agents › Multi-agent systems › multi-agent coordination
multi-agent routing
0.912025
A Flow Based Planning Method for Multi-Agent Progression with Deployable Agents and Communication Constraints · ICAPS 2025
Graph algorithms and graph theory › graph algorithms
network flow
0.912025
A Flow Based Planning Method for Multi-Agent Progression with Deployable Agents and Communication Constraints · ICAPS 2025

Methods — techniques the papers use, named apart from their topics

network flow model · 1.7constraint satisfaction problem · 1.7
YearPublicationVenuePosition
2025 A Flow Based Planning Method for Multi-Agent Progression with Deployable Agents and Communication Constraints
abstract
This paper deals with the problem of planning multiple agent movements through a mission area modeled as a graph. The agents undergo classic communication and temporal constraints, and the quantitative objective is the minimization of the team’s traversal makespan. Additional specificities make the problem a particularly complex routing one: on some nodes are associated durative and coordinated actions to perform, which can involve either the co-presence of several agents or time dependencies. Also, some agents are deployable and able to move on denser graphs: namely, aerial robots can take off and land on the ground vehicle at any planned position, and can fly above ground obstacles. We model the problem as a CSP and solve it with a network flow model. Results show the efficacy of the model and resolution scheme, which provides solutions with one or two orders of magnitude smaller time than a numerical temporal hierarchical planning model, with only a few percent loss of optimality.
Emile Siboulet, Roland Godet, Arthur Bit-Monnot, Marc-Emmanuel Coupvent des Graviers, Christophe Guettier, Simon Lacroix
ICAPS1