Learning Probabilistic Responsibility Allocations for Multi-Agent Interactions
Human behavior in interactive settings is shaped not only by individual objectives but also by shared constraints with others, such as safety. Understanding how people allocate responsibility, i.e., how much one deviates from their desired policy to accommodate others, can inform the design of socially compliant and trustworthy autonomous systems. In this work, we introduce a method for learning a probabilistic responsibility allocation model that captures the multimodal uncertainty inherent in multi-agent interactions. Specifically, our approach leverages the latent space of a conditional variational autoencoder, combined with techniques from multi-agent trajectory forecasting, to learn ...
| Authors | Isaac Remy, Caleb Chang, Karen Leung |
|---|---|
| Primary category | cs.MA |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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