DIDER: Discovering Interpretable Dynamically Evolving Relations

TL;DR

DIDER is a novel framework for modeling evolving multiagent interactions that emphasizes intrinsic interpretability and improves trajectory prediction by disentangling interaction types and durations.

Contribution

It introduces an end-to-end method that discovers interpretable, evolving relations without post-hoc analysis, enhancing both interpretability and prediction accuracy.

Findings

DIDER achieves intrinsic interpretability of dynamic relations.

Modeling disentangled relations improves trajectory forecasting.

DIDER performs well on both synthetic and real-world datasets.

Abstract

Effective understanding of dynamically evolving multiagent interactions is crucial to capturing the underlying behavior of agents in social systems. It is usually challenging to observe these interactions directly, and therefore modeling the latent interactions is essential for realizing the complex behaviors. Recent work on Dynamic Neural Relational Inference (DNRI) captures explicit inter-agent interactions at every step. However, prediction at every step results in noisy interactions and lacks intrinsic interpretability without post-hoc inspection. Moreover, it requires access to ground truth annotations to analyze the predicted interactions, which are hard to obtain. This paper introduces DIDER, Discovering Interpretable Dynamically Evolving Relations, a generic end-to-end interaction modeling framework with intrinsic interpretability. DIDER discovers an interpretable sequence of inter-agent interactions by disentangling the task of latent interaction prediction into sub-interaction prediction and duration estimation. By imposing the consistency of a sub-interaction type over an extended time duration, the proposed framework achieves intrinsic interpretability without requiring any post-hoc inspection. We evaluate DIDER on both synthetic and real-world datasets. The experimental results demonstrate that modeling disentangled and interpretable dynamic relations improves performance on trajectory forecasting tasks.