When Shall I Estimate Your Intent? Costs and Benefits of Intent Inference in Multi-Agent Interactions

Sunny Amatya, Mukesh Ghimire, Yi Ren, Zhe Xu, Wenlong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper addresses incomplete-information dynamic games, where reward parameters of agents are private. Previous studies have shown that online belief update is necessary for deriving equilibrial policies of such games, especially for high-risk games such as vehicle interactions. However, updating beliefs in real time is computationally expensive as it requires continuous computation of Nash equilibria of the sub-games starting from the current states. In this paper, we consider the triggering mechanism of belief update as a policy defined on the agents' physical and belief states, and propose learning this policy through reinforcement learning (RL). Using a two-vehicle uncontrolled intersection case, we show that intermittent belief update via RL is sufficient for safe interactions, reducing the computation cost of updates by 59% when agents have full observations of physical states. Simulation results also show that the belief update frequency will increase as noise becomes more significant in measurements of the vehicle positions.

Original languageEnglish (US)
Title of host publication2022 American Control Conference, ACC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781665451963
StatePublished - 2022
Event2022 American Control Conference, ACC 2022 - Atlanta, United States
Duration: Jun 8 2022Jun 10 2022

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2022 American Control Conference, ACC 2022
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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