DeepAI AI Chat
Log In Sign Up

Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network

by   Maosen Li, et al.

This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system. We propose a novel collaborative prediction unit (CoPU), which aggregates the predictions from multiple collaborative predictors according to a collaborative graph. Each collaborative predictor is trained to predict the status of an agent by considering the impact of another agent. The edge weights of the collaborative graph reflect the importance of each predictor. The collaborative graph is adjusted online by multiplicative update, which can be motivated by minimizing an explicit objective. With this objective, we also conduct regret analysis to indicate that, along with training, our CoPU achieves similar performance with the best individual collaborative predictor in hindsight. This theoretical interpretability distinguishes our method from many other graph networks. To progressively refine predictions, multiple CoPUs are stacked to form a collaborative graph neural network. Extensive experiments are conducted on three tasks: online simulated trajectory prediction, online human motion prediction and online traffic speed prediction, and our methods outperform state-of-the-art works on the three tasks by 28.6 respectively.


page 1

page 12

page 14

page 15


Learning to Coordinate via Multiple Graph Neural Networks

The collaboration between agents has gradually become an important topic...

CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks

Cascade prediction aims at modeling information diffusion in the network...

Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting

In multi-modal multi-agent trajectory forecasting, two major challenges ...

When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach

Personalization lies at the core of boosting the product search system p...

Online Paging with a Vanishing Regret

This paper considers a variant of the online paging problem, where the o...

Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning

Recently, deploying deep neural network (DNN) models via collaborative i...