Proposes Mutual Information Surprise (MIS) framework and reaction policy using sampling adjustment and process forking that outperforms classical surprise measures on synthetic and pollution estimation tasks.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
Active inference model unifies human collision avoidance by reproducing meta-analysis aggregates and simulator-specific effects on response timing, maneuver selection, and execution.
citing papers explorer
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Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems
Proposes Mutual Information Surprise (MIS) framework and reaction policy using sampling adjustment and process forking that outperforms classical surprise measures on synthetic and pollution estimation tasks.
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Active inference as a unified model of collision avoidance behavior in human drivers
Active inference model unifies human collision avoidance by reproducing meta-analysis aggregates and simulator-specific effects on response timing, maneuver selection, and execution.