On Human Assisted Decision Making for Machines Using Correlated Observations

Nandan Sriranga, Baocheng Geng, Pramod K. Varshney

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

Abstract

In this paper, the aim is to model the dependence between a continuous machine observation and a discrete human decision maker using copula theory for a binary hypothesis testing problem. We use a copula-based Likelihood Ratio Test (LRT) and derive expressions for the probability of false alarm and the probability of detection when the Signal-to-Noise ratio (SNR) is sufficiently large. When the machine observations are Gaussian with shifted means under the two hypotheses, we show the nature of the region in which the machine's observation falls, where there is no need for human assistance. We show that if the SNR is sufficiently large, the region that requires the human decision is a continuous interval.

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
Pages1502-1506
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

  • binary decision making
  • copula theory
  • Human-machine collaboration
  • information fusion

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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