Leadership Inference for Multi-Agent Interactions

Effectively predicting intent and behavior requires inferring leadership in multi-agent interactions. Dynamic games provide an expressive theoretical framework for modeling these interactions. Employing this framework, we propose a novel method to infer the leader in a two-agent game by observing th...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2024-05, Vol.9 (5), p.1-8
Main Authors: Khan, Hamzah I., Fridovich-Keil, David
Format: Article
Language:English
Subjects:
Costs
Game theory
Games
Heuristic algorithms
Iterative algorithms
Iterative methods
Leadership
Leadership Inference
Multiagent systems
Nonlinear dynamical systems
Nonlinear dynamics
Optimization and Optimal Control
Probabilistic Inference
Stackelberg Games
Trajectory
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Summary:Effectively predicting intent and behavior requires inferring leadership in multi-agent interactions. Dynamic games provide an expressive theoretical framework for modeling these interactions. Employing this framework, we propose a novel method to infer the leader in a two-agent game by observing the agents' behavior in complex, long-horizon interactions. We make two contributions. First, we introduce an iterative algorithm that solves dynamic two-agent Stackelberg games with nonlinear dynamics and nonquadratic costs, and demonstrate that it consistently converges in repeated trials. Second, we propose the Stackelberg Leadership Filter (SLF), an online method for identifying the leading agent in interactive scenarios based on observations of the game interactions. We validate the leadership filter's efficacy on simulated driving scenarios to demonstrate that the SLF can draw conclusions about leadership that match right-of-way expectations.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3381469