Neural-network agents trained in social environments learn hybrid navigation strategies that combine individual landmark use with social following, with strategy shifts driven by the ratio of skilled to unskilled social agents.
Analyzing Collective Motion with Machine Learning and Topology
3 Pith papers cite this work, alongside 44 external citations. Polarity classification is still indexing.
3
Pith papers citing it
44
external citations · OpenAlex
years
2026 3representative citing papers
Time-delayed CROCKERs from persistent homology of simulated pedestrian positions separate corridor flow regimes by inflow parameters up to symmetry.
The authors combine topological data analysis and multi-objective Bayesian inference to achieve practical parameter identifiability and identify simpler rules in an agent-based model of zebrafish patterns.
citing papers explorer
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Social-spatial dependencies for learning visual navigation
Neural-network agents trained in social environments learn hybrid navigation strategies that combine individual landmark use with social following, with strategy shifts driven by the ratio of skilled to unskilled social agents.
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A study of holes: Topological analysis reveals crowd dynamics regimes in a bidirectional corridor scenario
Time-delayed CROCKERs from persistent homology of simulated pedestrian positions separate corridor flow regimes by inflow parameters up to symmetry.
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Multi-objective Bayesian inference in an agent-based model of zebrafish patterns via topological data analysis
The authors combine topological data analysis and multi-objective Bayesian inference to achieve practical parameter identifiability and identify simpler rules in an agent-based model of zebrafish patterns.