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Thursday, November 19, 2020

Modeling Multi Agent Interacting Systems

 Fascinating piece.  Aren't we all multi agent interacting systems?  Can we effectively model that to understand it in context?   Berkeley Bair takes a took.  Its hard.  Considerable technical challenge.  Starting with urban traffic models.   Early on we used agent modeling to understand how new markets changed.   

EvolveGraph: Dynamic Neural Relational Reasoning for Interacting Systems

Jiachen Li    Nov 18, 2020

Multi-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems. The interactions between entities / components can give rise to very complex behavior patterns at the level of both individuals and the multi-agent system as a whole. Since usually only the trajectories of individual entities are observed without any knowledge of the underlying interaction patterns, and there are usually multiple possible modalities for each agent with uncertainty, it is challenging to model their dynamics and forecast their future behaviors.

Figure 1. Typical multi-agent interacting systems.

In many real-world applications (e.g. autonomous vehicles, mobile robots), an effective understanding of the situation and accurate trajectory prediction of interactive agents play a significant role in downstream tasks, such as decision making and planning. We introduce a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents. Considering the uncertainty of future behaviors, the model is designed to provide multi-modal prediction hypotheses. Since the underlying interactions may evolve even with abrupt changes over time, and different modalities of evolution may lead to different outcomes, we address the necessity of dynamic relational reasoning and adaptively evolving the interaction graphs.

Challenges of Multi-Agent Behavior Prediction

Figure 2. An illustration of a typical urban intersection scenario.

We use an urban intersection scenario with multiple interacting traffic participants as an illustrative example to elaborate on the major challenges of the multi-agent behavior prediction task.

First, there may be heterogeneous agents that have distinct behavior patterns, thus using a homogeneous dynamics / behavior model may not be sufficient. For example, there are different constraints and traffic rules for vehicles and pedestrians. More specifically, vehicle trajectories are strictly constrained by road geometry and their own kinematic models; while pedestrian behaviors are much more flexible.  ... ' 

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