Multi Agent Pathfinding Professors in Global

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Northwestern University United States of America
agent-based modeling
multi-level models
multi-method research design
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Zinovi Rabinovich
Carleton University Canada
multi-agent learning
multi-agent systems
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Maria Polukarov
King's College London United Kingdom
multi-agent systems
multi-agent reinforcement learning
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Brandon Haworth
University of Victoria Canada
multi-agent reinforcement learning
agent-based modelling
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Mithun Chakraborty
New York University United States of America
multiagent systems
agent-based modeling
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Erandi Hene Kankanamge
University of New South Wales Australia
multi-agent systems
multi-objective optimization
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Bouchra Nasri
University of Montreal Canada
multilevel analysis
multivariate analysis
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Manuel Lopez-ibanez
University of Manchester United Kingdom
multi-objective optimisation
multi-criteria decision making
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Olaf Schenk
Università della Svizzera italiana Switzerland
multicore algorithms
manycore algorithms
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Olaf Schenk
Università della Svizzera italiana Switzerland
multicore algorithms
manycore algorithms
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Akke Suiker
Eindhoven University of Technology Belgium
multi-physics modeling
multi-scale modeling
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Gerard Gorman
Imperial College London United Kingdom
multi-physics modeling
multi-scale modeling
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Yung Shin
Purdue University United States of America
multi-physics modeling
multi-scale modeling
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Panagiotis Angeloudis
Imperial College London United Kingdom
multi-agent modeling
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Sara Kalvala
University of Warwick United Kingdom
multi-agent modeling
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Minghui Zhu
Pennsylvania State University United States of America
multi-agent networks
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Manxi Wu
University of California, Berkeley United States of America
multi-agent learning
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Jakob Foerster
University of Oxford United Kingdom
multi-agent learning
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Sindri Magnússon
Stockholm University Sweden
multi-agent learning
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Georgios Piliouras
Singapore University of Technology and Design Singapore
multi-agent learning
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Manxi Wu
University of California, Berkeley United States of America
multi-agent learning
decentralized multi-agent reinforcement learning
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