Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy

Multi-agent, collaborative sensor fusion is a vital component of a multi-national intelligence toolkit. In safety-critical and/or contested environments, adversaries may infiltrate and compromise a number of agents. We analyze state of the art multi-target tracking algorithms under this compromised...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-03
Main Authors: R Spencer Hallyburton, Pajic, Miroslav
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Multiagent systems
Multiple target tracking
Safety critical
Trustworthiness
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator R Spencer Hallyburton
Pajic, Miroslav
description Multi-agent, collaborative sensor fusion is a vital component of a multi-national intelligence toolkit. In safety-critical and/or contested environments, adversaries may infiltrate and compromise a number of agents. We analyze state of the art multi-target tracking algorithms under this compromised agent threat model. We prove that the track existence probability test ("track score") is significantly vulnerable to even small numbers of adversaries. To add security awareness, we design a trust estimation framework using hierarchical Bayesian updating. Our framework builds beliefs of trust on tracks and agents by mapping sensor measurements to trust pseudomeasurements (PSMs) and incorporating prior trust beliefs in a Bayesian context. In case studies, our trust estimation algorithm accurately estimates the trustworthiness of tracks/agents, subject to observability limitations.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2986606746</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2986606746</sourcerecordid><originalsourceid>FETCH-proquest_journals_29866067463</originalsourceid><addsrcrecordid>eNqNyr0KwjAUQOEgCBbtO1xwLsSkTetmLYpLt-4lxVRTYq7mR-jb6-ADOJ3hOwuSMM53WZUztiKp9xOllImSFQVPyOEoZ-W1tNCqcMerhxEddC76ANpCg8bIAZ0M-q2gjSborL4pG6COAS0-5g1ZjtJ4lf66JtvzqWsu2dPhKyof-gmjs1_q2b4SgooyF_y_6wMvrjkb</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2986606746</pqid></control><display><type>article</type><title>Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy</title><source>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</source><creator>R Spencer Hallyburton ; Pajic, Miroslav</creator><creatorcontrib>R Spencer Hallyburton ; Pajic, Miroslav</creatorcontrib><description>Multi-agent, collaborative sensor fusion is a vital component of a multi-national intelligence toolkit. In safety-critical and/or contested environments, adversaries may infiltrate and compromise a number of agents. We analyze state of the art multi-target tracking algorithms under this compromised agent threat model. We prove that the track existence probability test ("track score") is significantly vulnerable to even small numbers of adversaries. To add security awareness, we design a trust estimation framework using hierarchical Bayesian updating. Our framework builds beliefs of trust on tracks and agents by mapping sensor measurements to trust pseudomeasurements (PSMs) and incorporating prior trust beliefs in a Bayesian context. In case studies, our trust estimation algorithm accurately estimates the trustworthiness of tracks/agents, subject to observability limitations.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Algorithms ; Bayesian analysis ; Multiagent systems ; Multiple target tracking ; Safety critical ; Trustworthiness</subject><ispartof>arXiv.org, 2024-03</ispartof><rights>2024. This work is published under http://creativecommons.org/licenses/by-nc-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2986606746?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>780,784,25753,37012,44590</link.rule.ids></links><search><creatorcontrib>R Spencer Hallyburton</creatorcontrib><creatorcontrib>Pajic, Miroslav</creatorcontrib><title>Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy</title><title>arXiv.org</title><description>Multi-agent, collaborative sensor fusion is a vital component of a multi-national intelligence toolkit. In safety-critical and/or contested environments, adversaries may infiltrate and compromise a number of agents. We analyze state of the art multi-target tracking algorithms under this compromised agent threat model. We prove that the track existence probability test ("track score") is significantly vulnerable to even small numbers of adversaries. To add security awareness, we design a trust estimation framework using hierarchical Bayesian updating. Our framework builds beliefs of trust on tracks and agents by mapping sensor measurements to trust pseudomeasurements (PSMs) and incorporating prior trust beliefs in a Bayesian context. In case studies, our trust estimation algorithm accurately estimates the trustworthiness of tracks/agents, subject to observability limitations.</description><subject>Algorithms</subject><subject>Bayesian analysis</subject><subject>Multiagent systems</subject><subject>Multiple target tracking</subject><subject>Safety critical</subject><subject>Trustworthiness</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNyr0KwjAUQOEgCBbtO1xwLsSkTetmLYpLt-4lxVRTYq7mR-jb6-ADOJ3hOwuSMM53WZUztiKp9xOllImSFQVPyOEoZ-W1tNCqcMerhxEddC76ANpCg8bIAZ0M-q2gjSborL4pG6COAS0-5g1ZjtJ4lf66JtvzqWsu2dPhKyof-gmjs1_q2b4SgooyF_y_6wMvrjkb</recordid><startdate>20240325</startdate><enddate>20240325</enddate><creator>R Spencer Hallyburton</creator><creator>Pajic, Miroslav</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20240325</creationdate><title>Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy</title><author>R Spencer Hallyburton ; Pajic, Miroslav</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_29866067463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Bayesian analysis</topic><topic>Multiagent systems</topic><topic>Multiple target tracking</topic><topic>Safety critical</topic><topic>Trustworthiness</topic><toplevel>online_resources</toplevel><creatorcontrib>R Spencer Hallyburton</creatorcontrib><creatorcontrib>Pajic, Miroslav</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>R Spencer Hallyburton</au><au>Pajic, Miroslav</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy</atitle><jtitle>arXiv.org</jtitle><date>2024-03-25</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>Multi-agent, collaborative sensor fusion is a vital component of a multi-national intelligence toolkit. In safety-critical and/or contested environments, adversaries may infiltrate and compromise a number of agents. We analyze state of the art multi-target tracking algorithms under this compromised agent threat model. We prove that the track existence probability test ("track score") is significantly vulnerable to even small numbers of adversaries. To add security awareness, we design a trust estimation framework using hierarchical Bayesian updating. Our framework builds beliefs of trust on tracks and agents by mapping sensor measurements to trust pseudomeasurements (PSMs) and incorporating prior trust beliefs in a Bayesian context. In case studies, our trust estimation algorithm accurately estimates the trustworthiness of tracks/agents, subject to observability limitations.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2024-03
issn 2331-8422
language eng
recordid cdi_proquest_journals_2986606746
source Publicly Available Content Database (Proquest) (PQ_SDU_P3)
subjects Algorithms
Bayesian analysis
Multiagent systems
Multiple target tracking
Safety critical
Trustworthiness
title Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T00%3A30%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Bayesian%20Methods%20for%20Trust%20in%20Collaborative%20Multi-Agent%20Autonomy&rft.jtitle=arXiv.org&rft.au=R%20Spencer%20Hallyburton&rft.date=2024-03-25&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2986606746%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_29866067463%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2986606746&rft_id=info:pmid/&rfr_iscdi=true