Online Relational Inference for Evolving Multi-agent Interacting Systems

• URL: http://arxiv.org/abs/2411.01442v2
• Date: Thu, 07 Nov 2024 05:54:07 GMT
• Title: Online Relational Inference for Evolving Multi-agent Interacting Systems
• Authors: Beomseok Kang, Priyabrata Saha, Sudarshan Sharma, Biswadeep Chakraborty, Saibal Mukhopadhyay,
• Abstract summary: Online Inference (ORI) is designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems. Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model with each new data point. A key innovation is the use of an adjacency matrix as a trainable parameter, optimized through a new adaptive learning technique called AdaRelation.
• Score: 14.275434303742328
• License:
• Abstract: We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using streaming data. Unlike traditional offline methods that rely on a fixed training set, ORI employs online backpropagation, updating the model with each new data point, thereby allowing it to adapt to changing environments in real-time. A key innovation is the use of an adjacency matrix as a trainable parameter, optimized through a new adaptive learning rate technique called AdaRelation, which adjusts based on the historical sensitivity of the decoder to changes in the interaction graph. Additionally, a data augmentation method named Trajectory Mirror (TM) is introduced to improve generalization by exposing the model to varied trajectory patterns. Experimental results on both synthetic datasets and real-world data (CMU MoCap for human motion) demonstrate that ORI significantly improves the accuracy and adaptability of relational inference in dynamic settings compared to existing methods. This approach is model-agnostic, enabling seamless integration with various neural relational inference (NRI) architectures, and offers a robust solution for real-time applications in complex, evolving systems.

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