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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F10%3A00177699%21RIV13-MSM-21230___
rdf:type
n3:Vysledek skos:Concept
rdfs:seeAlso
http://www.springerlink.com/content/978-3-642-15419-5#section=759276&page=1&locus=0
dcterms:description
We present a method for learning characteristic motion patterns of mobile agents. The method works on two levels. On the first level, it uses the expectation-maximization algorithm to build a Gaussian mixture model of the spatial density of agents' movement. On the second level, agents' trajectories as expressed as sequences of the components of the mixture model; the sequences are subsequently used to train hidden Markov models. The trained hidden Markov models are then employed to determine agent type, predict further agent movement or detect anomalous agents. The method has been evaluated in the maritime domain using ship trajectory data generated by the AgentC maritime traffic simulation. We present a method for learning characteristic motion patterns of mobile agents. The method works on two levels. On the first level, it uses the expectation-maximization algorithm to build a Gaussian mixture model of the spatial density of agents' movement. On the second level, agents' trajectories as expressed as sequences of the components of the mixture model; the sequences are subsequently used to train hidden Markov models. The trained hidden Markov models are then employed to determine agent type, predict further agent movement or detect anomalous agents. The method has been evaluated in the maritime domain using ship trajectory data generated by the AgentC maritime traffic simulation.
dcterms:title
Probabilistic Modeling of Mobile Agents' Trajectories Probabilistic Modeling of Mobile Agents' Trajectories
skos:prefLabel
Probabilistic Modeling of Mobile Agents' Trajectories Probabilistic Modeling of Mobile Agents' Trajectories
skos:notation
RIV/68407700:21230/10:00177699!RIV13-MSM-21230___
n4:aktivita
n15:Z
n4:aktivity
Z(MSM6840770038)
n4:dodaniDat
n13:2013
n4:domaciTvurceVysledku
n5:4394674 n5:2490013 n5:8733198
n4:druhVysledku
n20:D
n4:duvernostUdaju
n14:S
n4:entitaPredkladatele
n19:predkladatel
n4:idSjednocenehoVysledku
282096
n4:idVysledku
RIV/68407700:21230/10:00177699
n4:jazykVysledku
n23:eng
n4:klicovaSlova
multi-agent learning; maritime transport; security
n4:klicoveSlovo
n16:maritime%20transport n16:multi-agent%20learning n16:security
n4:kontrolniKodProRIV
[8FAC18B168AA]
n4:mistoKonaniAkce
Mississippi
n4:mistoVydani
Berlin
n4:nazevZdroje
Agents and Data Mining Interaction
n4:obor
n17:JC
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:rokUplatneniVysledku
n13:2010
n4:tvurceVysledku
Pěchouček, Michal Jakob, Michal Urban, Štěpán
n4:typAkce
n22:WRD
n4:wos
000286775400006
n4:zahajeniAkce
2010-04-08+02:00
n4:zamer
n12:MSM6840770038
s:issn
0302-9743
s:numberOfPages
12
n10:doi
10.1007/978-3-642-15420-1_6
n8:hasPublisher
Springer-Verlag
n6:isbn
978-3-642-15419-5
n18:organizacniJednotka
21230