Distributed Resource Allocation for Human-Autonomy Teaming With Human Preference Uncertainty

This letter investigates distributed resource allocation involving multiple autonomous agents and multiple humans, focusing on two challenges: (i) the dependency between autonomous and human agents through interaction; (ii) accounting for human uncertainties where both parties must collectively sati...

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Bibliographic Details
Published in:IEEE control systems letters Vol. 9; pp. 2327 - 2332
Main Authors: Yao, Yichen, Mbagna Nanko, Ryan, Wang, Yue, Wang, Xuan
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Autonomous agents
Decision making
Discrete Fourier transforms
Distributed algorithms
Distributed resource allocation
Gaussian distribution
human preference uncertainty
human-autonomy teaming
Probabilistic logic
Resource management
Uncertainty
Vectors
ISSN:2475-1456, 2475-1456
Online Access:Get full text
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Summary:This letter investigates distributed resource allocation involving multiple autonomous agents and multiple humans, focusing on two challenges: (i) the dependency between autonomous and human agents through interaction; (ii) accounting for human uncertainties where both parties must collectively satisfy globally coupled probabilistic resource constraints. To address these, we first quantify the distribution of human choice behaviors using the maximum likelihood estimation (MLE), where human decisions evolve in response to nearby agent behaviors. Building on this human model, we introduce a novel reformulation that approximates the original probabilistic constraint via a polyhedral inner approximation, which then enables a fully distributed algorithm design over the system's communication graph while ensuring probabilistic constraint satisfaction. The proposed approach is validated through theoretical analysis and human-subject experiments.
ISSN:2475-1456
2475-1456
DOI:10.1109/LCSYS.2025.3604959