Summary of Online Relational Inference For Evolving Multi-agent Interacting Systems, by Beomseok Kang et al.
Online Relational Inference for Evolving Multi-agent Interacting Systems
by Beomseok Kang, Priyabrata Saha, Sudarshan Sharma, Biswadeep Chakraborty, Saibal Mukhopadhyay
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Online Relational Inference (ORI), a novel framework that efficiently identifies hidden interaction graphs in multi-agent interacting systems using streaming data. Unlike traditional offline methods, ORI employs online backpropagation to adapt to changing environments in real-time. The framework utilizes an adjacency matrix as a trainable parameter optimized through AdaRelation, a new adaptive learning rate technique. Additionally, Trajectory Mirror (TM) is introduced to improve generalization by exposing the model to varied trajectory patterns. Experimental results on synthetic and real-world datasets demonstrate that ORI significantly improves relational inference accuracy and adaptability in dynamic settings compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to understand how people move together, like in a dance or sports team. They use computer algorithms to figure out the hidden rules behind their movements. This is different from other approaches because it can learn and update itself as the situation changes, like if someone gets hurt and needs to adjust their movement. The algorithm uses an “adjacency matrix” that helps it understand how people move relative to each other. They also use a technique called Trajectory Mirror to make the algorithm better at understanding different types of movements. The results show that this approach is much better than others at figuring out the hidden rules behind human movement. |
Keywords
» Artificial intelligence » Backpropagation » Generalization » Inference
