Learning Probabilistic Responsibility Allocations for Multi-Agent Interactions

TL;DR

This paper presents a novel probabilistic model for understanding responsibility distribution in multi-agent interactions, using a conditional variational autoencoder to handle uncertainty and interpret shared responsibilities.

Contribution

It introduces a new method combining VAEs and control optimization to learn responsibility allocations without ground-truth labels, enhancing interpretability and prediction in multi-agent systems.

Findings

Achieves strong predictive performance on the INTERACTION dataset.

Provides interpretable insights into responsibility patterns.

Handles multimodal uncertainty in responsibility allocation.

Abstract

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 a distribution over responsibility allocations conditioned on scene and agent context. Although ground-truth responsibility labels are unavailable, the model remains tractable by incorporating a differentiable optimization layer that maps responsibility allocations to induced controls, which are available. We evaluate our method on the INTERACTION driving dataset and demonstrate that it not only achieves strong predictive performance but also provides interpretable insights, through the lens of responsibility, into patterns of multi-agent interaction.