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Issa K. Hanou

dblp:357/0358 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0001-5873-5139ORCID · reported

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 · 67% Motion planning and robot control · 33%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
classical planning
0.812024
Replanning in Advance for Instant Delay Recovery in Multi-Agent Applications: Rerouting Trains in a Railway Hub · ICAPS 2024
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
multi-agent planning
0.812024
Replanning in Advance for Instant Delay Recovery in Multi-Agent Applications: Rerouting Trains in a Railway Hub · ICAPS 2024
Robotics › Motion planning and robot control › path planning › search-based path planning
safe interval path planning
0.812024
Replanning in Advance for Instant Delay Recovery in Multi-Agent Applications: Rerouting Trains in a Railway Hub · ICAPS 2024

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

safe interval planning · 0.8any-start-time planning · 0.8
YearPublicationVenuePosition
2024 Replanning in Advance for Instant Delay Recovery in Multi-Agent Applications: Rerouting Trains in a Railway Hub
abstract
Train routing is sensitive to delays that occur in the network. When a train is delayed, it is imperative that a new plan be found quickly, or else other trains may need to be stopped to ensure safety, potentially causing cascading delays. In this paper, we consider this class of multi-agent planning problems, which we call Multi-Agent Execution Delay Replanning. We show that these can be solved by reducing the problem to an any-start-time safe interval planning problem. When an agent has an any-start-time plan, it can react to a delay by simply looking up the precomputed plan for the delayed start time. We identify crucial real-world problem characteristics like the agent's speed, size, and safety envelope, and extend the any-start-time planning to account for them. Experimental results on real-world train networks show that any-start-time plans are compact and can be computed in reasonable time while enabling agents to instantly recover a safe plan.
Issa K. Hanou, Devin Wild Thomas, Wheeler Ruml, Mathijs de Weerdt
ICAPS1