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researcher

L. Cohen

11 papers hereh-index 172k citations69 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author7
  • last author1

Across the 10 of 11 papers where every author was matched, so the position is known.

fields
  • cs.AI7
  • cs.LG3
  • cs.LO1
same name
  • L. Cohen — 41 papers, h 49
  • L. Cohen — 12 papers, h 42
  • L. Cohen — 8 papers, h 9
  • L. Cohen — 7 papers, h 5
  • L. Cohen — 5 papers, h 35
  • L. Cohen — 5 papers, h 19

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152023
most citedOverview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

91 citations · 130 across the 7 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.AI2017★ 34 cited

Feasibility Study: Moving Non-Homogeneous Teams in Congested Video Game Environments

Hang Ma, Jingxing Yang, Liron Cohen +2

Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations. I…

cs.AI2017

Rapid Randomized Restarts for Multi-Agent Path Finding Solvers

Liron Cohen, Glenn Wagner, T. K. Satish Kumar +2

Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in artificial intelligence and robotics. It has many real-world applications for which existing MAPF solvers use…

cs.AI2017

The FastMap Algorithm for Shortest Path Computations

Liron Cohen, Tansel Uras, Shiva Jahangiri +3

We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. The Euclidean distance between any two nodes in t…

cs.AI2017★ 91 cited

Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

Hang Ma, Sven Koenig, Nora Ayanian +7

Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research. We discuss issues that arise when genera…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.