Explo ing he Spa ial Densi y o S a egy Models in a Realis ic Dis ibu ed
In e ac i e Applica ion
Damien Ma shall, Declan Delaney
Depa men o Compu e Science,
Na ional Uni e si y o I eland, Maynoo h,
Co. Kilda e, Republic o I eland.
E-mail:{damienm, decland}@cs.may.ie
Seamus McLoone, Tomas Wa d
Depa men o Elec onic Enginee ing,
Na ional Uni e si y o I eland, Maynoo h,
Co. Kilda e, Republic o I eland.
E-mail: {seamus.mcloone, omas.wa d}@eeng.may.ie
Abs ac
As Dis ibu ed In e ac i e Applica ions (DIAs) become
inc easingly mo e p ominen in he ideo game indus y
hey mus scale o accommoda e p og essi ely mo e use s
and main ain a globally consis en wo ld iew. Howe e ,
ne wo k cons ain s, such as bandwid h, limi he amoun
o communica ion allowed be ween use s. Se e al
me hods o educing ne wo k communica ion packe s,
while main aining consis ency, exis . These include dead
eckoning and he hyb id s a egy-based modelling
app oach. This la e me hod combines a sho - e m
model such as dead eckoning wi h a long- e m s a egy
model o use beha iou . By employing he s a egy ha
mos closely ep esen s use beha iou , a educ ion in he
numbe o ne wo k packe s ha mus be ansmi ed o
main ain consis ency has been shown. In his pape a
no el me hod o cons uc ing mul iple long- e m
s a egies using dead eckoning and polygons is
desc ibed. Fu he mo e he algo i hms a e implemen ed
in an indus y-p o en game engine known as To que. A
se ies o expe imen s a e execu ed o in es iga e he
e ec s o a ying he spa ial densi y o s a egy models on
he numbe o packe s ha need o be ansmi ed o
main ain he global consis ency o he DIA. The esul s
show ha inc easing he spa ial densi y o s a egy
models allows a highe consis ency o be achie ed wi h
ewe packe s using he hyb id s a egy-based model han
wi h pu e dead eckoning. In some cases, he hyb id
s a egy-based model comple ely eplaces dead eckoning
as a means o communica ing upda es.
1. In oduc ion
Dis ibu ed compu e games a e becoming one o he
mo e impo an ace s o an al eady bu geoning games
indus y. Such games pe ain o a class o compu e
applica ion known as Dis ibu ed In e ac i e Applica ions
(DIAs), and can in ol e ens o housands o simul aneous
playe s in e ac ing in a simula ed en i onmen . Gi en he
scale o DIAs, he olume o da a ansmi ed can be
conside able, o example, E e ques [1]. As he
complexi y o he DIAs inc eases and he numbe o
concu en use s mul iplies, he applica ion and
connec ing ne wo k mus be able o scale wi hou losing
he eal- ime expe ience o pa icipa ing in a sha ed
en i onmen [2]. This abili y o scale is inhe en ly linked
o he numbe o ne wo k packe s ha need o be
ansmi ed be ween pa icipan s o main ain a consis en
global wo ld iew o he DIA. Se e al echniques exis o
educe he quan i y o ne wo k a ic, including ele ance
il e ing, comp ession and dead eckoning [3, 4].
Dead eckoning uses a sho - e m model o p edic
en i y dynamics. Howe e , i does no ake a p io i
in o ma ion ega ding en i y beha iou in ela ion o an
objec i e o goal in o accoun . In con as he ecen ly
p oposed hyb id s a egy-based modelling app oach
combines a sho - e m dead eckoning model wi h a long-
e m s a egy model [5]. Using his app oach a educ ion
in he numbe o packe s ha a e equi ed o main ain
global consis ency has been demons a ed compa ed o
using dead eckoning alone. These esul s we e based on
a es ic ed wo-dimensional es en i onmen ha se ed
o p o e he concep bu did no allow he expe imen s o
scale o la ge numbe s o s a egy models. The long- e m
s a egy models hemsel es we e cons uc ed using isual
heu is ics. S a egy models can be o wo ypes: an
explo a o y o ansien s a egy ha use s employ when
hey a e un amilia wi h he en i onmen and a s eady-
s a e s a egy, which co esponds o a p e e ed way o
sa is y he use goal in he en i onmen .
He e, a no el implemen a ion o he s a egy model
echnique in an indus y-p o en game engine called
To que is p esen ed [6]. This me hod builds on p e ious
wo k by acili a ing he cons uc ion o an a bi a y
numbe o s a egy models. Fu he mo e, simula ion
esul s a e p esen ed o expe imen s ea u ing a a ying
spa ial densi y o s a egy models using his new es
en i onmen . The esul s show ha as he numbe o
models inc eases, he e is a dec ease in he numbe o
packe s equi ed in o de o main ain consis ency in he
DIA. An op imum numbe o hese models exis o
s eady-s a e ajec o ies, abo e which no u he packe
educ ion is ob ained. The ac ual alue i sel will a y
om applica ion o applica ion.
2. Implemen ing he Hyb id S a egy-Based
Model
One o he key di icul ies wi h he hyb id s a egy-
based model app oach is he de e mina ion o he s a egy
models. To da e, ac ual use da a has been eco ded in a
limi ed game-like en i onmen and a ep esen a i e s eady
s a e s a egy was chosen based on a isual analysis o
ac ual use s eady-s a e ajec o ies[5].
He e, a no el app oach based on dead eckoning is
p oposed. When dead eckoning is employed, he e o
h eshold is always exceeded a ce ain key a eas. By
eco ding he poin s whe e he dead eckoning h eshold is
exceeded by his use , a se o poin s is ob ained ha
p o ides an a e age piecewise linea model o a use ’s
ajec o y – see Figu e 1.
The piecewise linea model is hen combined wi h he
e o h eshold alues which de ines bands on bo h sides
o he model o desc ibe a polygon shape known as a
“s a egy polygon”. An en i y loca ed wi hin he s a egy
polygon is deemed o be ollowing ha pa icula s a egy.
The i s s ep in he c ea ion o he s a egy polygon is
he de ini ion o he polygon edges. Each edge consis s o
a line pa allel o a segmen o he piecewise linea cu e.
The dis ance be ween an edge and he app op ia e
segmen o he cu e is he equi ed e o h eshold – see
Figu e 2a. Nex , he end poin s o he segmen s need o be
joined so as o p o ide a closed polygon. I his is
implemen ed inco ec ly, he esul ing polygon could be
dis o ed, leading o e oneous assump ions abou he
en i y’s beha iou . Again, see Figu e 2a.
The p oblem is a oided by conside ing wo con iguous
segmen s o he piecewise linea cu e in o de o de ine
he s a egy polygon e ices. The pa allel segmen s a e
c ea ed as de ailed abo e. Howe e , he e ices o he
s a egy polygon a e gi en by he loca ions whe e he
newly gene a ed pa allel segmen s in e sec . Examples o
such in e sec ions a e de ailed in Figu e 2b. Repea ed
applica ion o his echnique o all he segmen s o he
piecewise linea cu e esul s in a g oup o e ices ha
desc ibes he s a egy polygon.
This p ocess can be used in o de o de ine mul iple
s a egy models. Ini ially, he equi ed numbe o
s a egies is c ea ed. Fo he pu poses o his pape , he
newly c ea ed s a egies a e always pa allel o ha o he
o iginal piecewise linea cu e. The e o e, he p ocess by
which he polygon edges a e c ea ed can be used o de ine
each indi idual s a egy. The pa allel dis ance be ween
each cu e is he equi ed dis ance be ween s a egies as
de ailed in Figu e 3.
Nex , each cu e is con e ed in o a ull s a egy
polygon, using he equi ed e o h eshold. This h eshold
is always se o a alue g ea e han hal he dis ance
be ween s a egies in o de o p o ide an o e lap be ween
models, and he e o e a oid unnecessa y upda es due o
excessi e swi ching be ween models.
Wi h mul iple models, i becomes impo an o
de e mine which model, i any, he en i y’s cu en
posi ion esides in. The ‘poin con ainmen es ’ is used o
do his [7]. This es is used ex ensi ely in compu e
games in a p ocess known as polygon illing [8]. I wo ks
by ex ending a ho izon al line om he cu en en i y
posi ion and coun ing he numbe o in e sec ions be ween
his line and he edges o he polygon. An odd numbe o
in e sec ions indica es ha he posi ion is inside he
polygon. O he wise he posi ion is ou side i . The
E o Th eshold
Dis ance be ween
s a egies
Figu e 1 A sample piecewise linea model.
Dead Reckoning
Upda es
Use T ajec o y
Piecewise linea
cu e
Y- axis
X- axis
Figu e 3 Mul iple hyb id s a egy-based models
Figu e 2(a) Dis o ion can occu when c ea ing he
s a egy polygon (b) The polygon dis o ion is esol ed
using he in e sec ion poin s o pa allel segmen s
Piecewise Linea
Cu e
S a egy Polygon
Edge
In e sec ion
Poin
E o
Th eshold
(a) (b)
a
b
c
O e lap
be ween
s a egies
Piecewise
linea cu es
decision o make he swi ch be ween models is aken when
an upda e om he cu en ly employed model is equi ed.
The s a egy model app oach was implemen ed in
To que using he algo i hms desc ibed in his sec ion,
along wi h a i s o de dead- eckoning algo i hm. The
engine was also modi ied so ha use ajec o ies could be
eco ded and s eady s a e s a egies iden i ied. The
ollowing sec ion de ails he expe imen s pe o med using
hese models.
3. Expe imen al Simula ions
The expe imen al es pla o m was implemen ed using
he To que game engine and a ull clien -se e
a chi ec u e was employed. Each simula ion was ca ied
ou in a game en i onmen ha consis ed o a unique ou e
om a s a o a a ge posi ion. These posi ions emained
in a ian h oughou . The use ’ s goal was o na iga e om
he s a o he a ge posi ion in he sho es ime possible
[6]. The applica ion simula ed he numbe o packe s
ansmi ed in o de o main ain global consis ency wi hin
a ce ain h eshold e o ole ance. The e o h esholds
and sepa a ion dis ance be ween he s a egies is de ined
in To que game uni s. Each game uni co esponds o a
me e in dis ance wi hin he i ual en i onmen .
The i s se o expe imen s measu ed he numbe o
packe s ansmi ed as he numbe o s a egies was
inc eased and a cons an e o h eshold was main ained.
The second g oup o expe imen s ocused on inc easing
he densi y o s a egies wi hin he same a ea o he
en i onmen . This was achie ed by beginning wi h a
single s a egy wi h a la ge e o h eshold. This de ined
he a ea, and encompassed all eco ded use ajec o ies.
The e o h eshold was hen dec eased and he numbe o
s a egies inc eased so ha he s a egy models always
co e ed he same a ea.
A o al o six subjec s pa icipa ed in he expe imen s.
All use ajec o ies we e eco ded. The applica ion hen
employed his da a o simula e he gene a ion o upda e
packe s unde a ious expe imen al condi ions. The
simula ed en i y na iga ed he es le el using he
posi ional da a eco ded om each pa icipan . When he
a ge posi ion was eached, he ollowing da a was
eco ded o disk: ype o upda e, he posi ion whe e he
upda e occu ed, he o al numbe o upda es ansmi ed
o ha a emp , he ac ual en i y posi ion, he modelled
posi ion and he s a egy polygon e ices.
4. Resul s
He e, esul s a e p esen ed o he wo se s o
expe imen s ou lined in he p e ious sec ion. In all
expe imen s, bo h he Hyb id S a egy-Based Model (H),
and Dead Reckoning (D), a e conside ed. In e e y case,
U
n
deno es a es use
.
4.1 Cons an E o Th eshold
As discussed in sec ion 3, he i s se o expe imen s
concen a ed on he compa ison o pu e dead eckoning
wi h he hyb id s a egy-based model app oach as he
numbe o s a egies inc eased and he e o h eshold was
main ained cons an . Table 1 de ails esul s om wo es
use s who a e ep esen a i e o all use s. In his case an
e o h eshold o 6 game uni s o bo h dead eckoning
and he s a egy model, and a dis ance o 9 game uni s
be ween s a egies, is employed.
F om his able we can see ha as he use con e ges o
a s eady s a e ajec o y, he e is a signi ican educ ion in
packe s when compa ed o pu e dead eckoning. We can
also no e a educ ion in packe s sen as he numbe o
a ailable models inc eases. When a use i s en e s he
en i onmen , as exempli ied in ial a emp s 1 and 2 o
bo h use s, hey a e ollowing an explo a o y o ansien
ajec o y. In hese cases, he use is explo ing he
en i onmen , and dead eckoning is used ex ensi ely.
Wi h he ansien ajec o ies, as he numbe o models
inc eases, he numbe o s a egy packe s also inc eases.
This occu s because he e a e mo e models a ailable and
he e is inc eased likelihood ha he en i y will c oss back
and o h ac oss hem in ge ing o know he en i onmen
and accomplish he goal.
Once he use has become mo e amilia wi h he es
le el, as seen in ial 3, ajec o ies close o ha o he
s eady s a e s a egy a e adop ed. This leads o a no able
educ ion in upda es, and o e all be e pe o mance om
he hyb id model in compa ison o dead eckoning. When
a s eady s a e ajec o y is adop ed, he addi ion o models
abo e a ce ain quan i y has li le o no e ec on packe
upda e a es. In hese cases, he op imum numbe o
models o ha ajec o y has been ound. This can be
seen in ials 4 and 5 in bo h ables, whe e minimal packe
educ ion is no ed a e he use o 3 s a egy models. In he
inal a emp s o bo h use s dead eckoning is made
comple ely edundan .
A se ies o expe imen s we e also conduc ed ea u ing
an e o h eshold o 9 game uni s, and a dis ance o 12
T ial DR 1 Model 3 Models 5 Models
U1 U2 U1 U2 U1 U2 U1 U2
D D H H H H H H
1 20 30 16 28 16 29 16 31
2 17 30 11 28 12 29 12 31
3 12 19 9 17 5 16 5 15
4 12 11 6 8 5 9 5 10
5 12 12 5 1 5 1 5 1
Table 1 E o Th eshold 6 S a egy Wid h 9
game uni s be ween s a egies. Resul s simila o ha o
able 1 we e no ed. Due o space es ic ions, howe e ,
hey a e no p esen ed he e.
4.2 Va iable E o Th eshold
The nex se o esul s ocuses on an inc easing spa ial
densi y o s a egy models – see Table 2. In his case, we
ini ially s a wi h a single la ge model wi h an e o
h eshold o 36, and p og essi ely inc ease he numbe o
models ound wi hin he a ea occupied by ha model.
Table 2 ein o ces he ac ha he hyb id s a egy-
based model pe o ms be e han pu e dead eckoning.
As would be expec ed, he numbe o pu e dead eckoning
packe s inc eases as he e o h eshold dec eases. A
leas one packe will be equi ed ega dless o he model
used; ials 4 and 5 indica e his o he hyb id model. The
packe ansmi ed in hese ials e e s o he long- e m
s a egy model.
In ial 2 o Table 2, we can see ha , as he h eshold
dec eases, an inc ease in he spa ial densi y o models
occu s, and swi ching be ween s a egies becomes mo e
likely. This accoun s o he inc easing numbe o packe s
as he densi y o models inc eases.
The esul s indica e ha when a single model is used i
co e s all s eady-s a e ajec o ies as shown by he single
packe ansmi ed in ials 3 o 5 in Table 2. Howe e ,
he h eshold is e y high and consequen ly consis ency is
low. By inc easing he numbe o models wi hin he same
space, he alue o he h eshold dec eases wi hou
incu ing a signi ican inc ease in packe s sen . T ials 4
and 5 clea ly illus a e his. A igh e h eshold p o ides
g ea e consis ency. This is a clea illus a ion o he
consis ency- h oughpu adeo [4].
6. Conclusions and Fu u e Wo k
In his pape we ha e desc ibed a no el me hod o
cons uc ing mul iple use s a egies using dead eckoning
and polygons in a ealis ic DIA known as To que. This
was ca ied ou in o de o p o ide a eal wo ld
implemen a ion o he hyb id s a egy-based model, and o
es he e ec s o a ying spa ial densi y on he amoun o
packe s needed o main ain consis ency o a DIA.
Using his app oach, we ha e shown ha he hyb id
s a egy-based model pe o ms be e han pu e dead
eckoning. As he numbe o models inc eases, he e is a
educ ion in he numbe o packe s needed o s eady s a e
ajec o ies. In hese cases, an inc ease in he numbe o
models abo e a ce ain quan i y has a negligible e ec on
packe educ ion. The poin a which his occu s is he
op imum numbe o models. The ac ual op imum alue
will a y wi h he applica ion and he ou e ep esen ed by
he s eady s a e s a egy.
An inc ease in he densi y o s a egies in one a ea
esul s in he minimisa ion o dead eckoning packe s.
When he ajec o ies ha e con e ged o a s eady s a e, he
hyb id s a egy-based model can p o ide inc eased
consis ency using ewe packe s han dead eckoning
alone.
Fu u e wo k will in ol e he examina ion o he
psycho-pe cep ual e ec s o la ency on use s o
Dis ibu ed In e ac i e Applica ions o es ablish c i e ia
o choosing e o h eshold alues.
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T ial 1 Model 3 Models 5 Models
D H D H D H
1 10 6 20 10 24 16
2 8 8 16 14 18 18
3 5 1 11 5 15 7
4 5 1 8 3 11 5
5 4 1 6 1 9 1
Table 2 Va iable E o Th eshold Resul s o 1 Use