Abstract
In a typical consensus scenario, a group of adaptive agents (i.e., sensors, fusion nodes, people, etc.) iteratively interact with their neighbors and update their (current) states or opinions toward collectively estimating some phenomenon of interest without any global coordination. Understanding the dynamics of consensus formation in such settings, where the collective behavior is governed solely by the local interactions, is a topic of overlapping interest for researchers in multiple domains [1-19]. With the emergence of social networks as a dominant force in modern society, the importance of understanding the mathematical underpinnings that may lead to a consensus in such complex fusion environments [20] is becoming more and more important. However, in addition to agent interactions being ad hoc and/or dynamic with possibly asynchronous communications (i.e., delays), sensing in such environments are often distributed and involves a large numbers of heterogeneous sources that include a mix of both soft (i.e., human or human-based) and hard (i.e., conventional physics-based) sensors. Therefore, convergence analysis in such complex fusion networks is a challenging task due mainly to the unstructured environment and the difficulties associated with adequately modeling the agent opinions itself.
| Original language | English (US) |
|---|---|
| Title of host publication | Multisensor Data Fusion |
| Subtitle of host publication | From Algorithms and Architectural Design to Applications |
| Publisher | CRC Press |
| Pages | 363-381 |
| Number of pages | 19 |
| ISBN (Electronic) | 9781482263756 |
| ISBN (Print) | 9781482263749 |
| DOIs | |
| State | Published - Jan 1 2017 |
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ASJC Scopus subject areas
- Engineering(all)
- Physics and Astronomy(all)
Cite this
Dynamics of consensus formation among agent opinions. / Wickramarathne, Thanuka; Premaratne, Kamal; Murthi, Manohar.
Multisensor Data Fusion: From Algorithms and Architectural Design to Applications. CRC Press, 2017. p. 363-381.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Dynamics of consensus formation among agent opinions
AU - Wickramarathne, Thanuka
AU - Premaratne, Kamal
AU - Murthi, Manohar
PY - 2017/1/1
Y1 - 2017/1/1
N2 - In a typical consensus scenario, a group of adaptive agents (i.e., sensors, fusion nodes, people, etc.) iteratively interact with their neighbors and update their (current) states or opinions toward collectively estimating some phenomenon of interest without any global coordination. Understanding the dynamics of consensus formation in such settings, where the collective behavior is governed solely by the local interactions, is a topic of overlapping interest for researchers in multiple domains [1-19]. With the emergence of social networks as a dominant force in modern society, the importance of understanding the mathematical underpinnings that may lead to a consensus in such complex fusion environments [20] is becoming more and more important. However, in addition to agent interactions being ad hoc and/or dynamic with possibly asynchronous communications (i.e., delays), sensing in such environments are often distributed and involves a large numbers of heterogeneous sources that include a mix of both soft (i.e., human or human-based) and hard (i.e., conventional physics-based) sensors. Therefore, convergence analysis in such complex fusion networks is a challenging task due mainly to the unstructured environment and the difficulties associated with adequately modeling the agent opinions itself.
AB - In a typical consensus scenario, a group of adaptive agents (i.e., sensors, fusion nodes, people, etc.) iteratively interact with their neighbors and update their (current) states or opinions toward collectively estimating some phenomenon of interest without any global coordination. Understanding the dynamics of consensus formation in such settings, where the collective behavior is governed solely by the local interactions, is a topic of overlapping interest for researchers in multiple domains [1-19]. With the emergence of social networks as a dominant force in modern society, the importance of understanding the mathematical underpinnings that may lead to a consensus in such complex fusion environments [20] is becoming more and more important. However, in addition to agent interactions being ad hoc and/or dynamic with possibly asynchronous communications (i.e., delays), sensing in such environments are often distributed and involves a large numbers of heterogeneous sources that include a mix of both soft (i.e., human or human-based) and hard (i.e., conventional physics-based) sensors. Therefore, convergence analysis in such complex fusion networks is a challenging task due mainly to the unstructured environment and the difficulties associated with adequately modeling the agent opinions itself.
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U2 - 10.1201/b18851
DO - 10.1201/b18851
M3 - Chapter
SN - 9781482263749
SP - 363
EP - 381
BT - Multisensor Data Fusion
PB - CRC Press
ER -