Asymptotic Performance in Heterogeneous Human-machine Inference Networks

Chen Quan, Baocheng Geng, Pramod K. Varshney

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

Abstract

We analyze the asymptotic performance of likelihood ratio based collaborative human-machine decision making systems. Human agents are assumed to make threshold based local binary decisions, where the thresholds are considered as random variables. The proposed hybrid system consists of multiple human sub-populations, with the thresholds of each sub-population characterized by non-identically distributed random variables, and a limited number of machines (physical sensors) whose exact values of thresholds are known. For such a hybrid system, we derive the asymptotic performance at the fusion center in terms of Chernoff information. When available, the effect of side information for human sensors is also studied in this paper. Moreover, a budget-constrained human worker selection optimization problem is formulated to determine the optimal number of workers selected from each sub-population according to different costs so that the best decision making performance could be achieved.

Original languageEnglish (US)
Title of host publicationConference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages584-588
Number of pages5
ISBN (Electronic)9780738131269
DOIs
StatePublished - Nov 1 2020
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: Nov 1 2020Nov 5 2020

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2020-November
ISSN (Print)1058-6393

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period11/1/2011/5/20

Keywords

  • asymptotic performance
  • Chernoff information
  • decision making
  • heterogeneous collaborative human-machine networks
  • inference
  • information fusion

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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