MASSIS
– Mul i-Agen Sys em Simula ion o Indoo Scena ios–
Ra ael Pax
[email p o ec ed]
G ado en Ingenie ía en In o má ica
Facul ad de In o má ica
T abajo de Fin de G ado
Di ec o :
Juan Pa ón Mes as
[email p o ec ed]
Mad id, Junio 2015
Au ho iza ion Fo Dissemina ion And
Use
Au o izo a la Uni e sidad Complu ense de Mad id a di undi y u iliza
con ines académicos, no come ciales y mencionando exp esamen e a su au-
o , an o la p opia memo ia, como el código, la documen ación y/o el so -
wa e desa ollado.
Ra ael Pax
G acias po ayuda me con us consejos,
po o alece me con u apoyo,
po le an a me con us ánimos,
po con ia en mí.
G acias po aleg a me con us isas,
po enamo a me con u mi ada,
po con agia me u locu a,
po hace me eliz.
G acias po se ma a illosa, Pila .
Acknowledgemen s
This wo k has been been suppo ed by he Go e nmen o he Region o
Mad id h ough he esea ch p og amme MOSI-AGIL-CM (g an
P2013/ICE-3019, co- unded by EU S uc u al Funds FSE and FEDER), and
by he Spanish Minis y o Economy and Compe i i eness, wi h he p ojec
Social Ambien Assis ing Li ing - Me hods (SociAAL) (g an TIN2011-
28335-C02-01).
Resumen
Las p uebas de aplicaciones pa a en o nos in eligen es son una a ea di í-
cil. Requie en de la ins alación de senso es y ac uado es, los sis emas de co-
municación, so wa e de con ol y la pa icipación de pe sonas ep esen ando
di e en es oles. Es o es cos oso, an o en iempo como en sen ido económi-
co. Además, exis en muchas si uaciones que, po azones p ác icas, esul an
bas an e complicadas de p oba (si uaciones de eme gencia, po ejemplo).
Las he amien as de simulación pueden se i de conside able ayuda pa a el
desa ollo de en o nos in eligen es. Uno de los aspec os más ele an es a e-
ne en cuen a en es e ipo de p uebas es el compo amien o humano y social
de los indi iduos, cuando se simula la o ma de cómo las pe sonas in e ac ú-
an con su en o no, incluyendo o os indi iduos. Si se u iliza un amewo k
de simulación mul iagen e pa a es os p opósi os, debe cons a de un modelo
cla o de agen e, cuyos mé odos de azonamien o puedan se diseñados desde
un ni el de abs acción más al o, que pueda ans o ma se en una imple-
men ación de o ma sencilla.
És e es uno de los p opósi os p incipales de MASSIS (Mul i-agen sys em
simula ion o InDoo Scena ios), un amewo k de simulación mul iagen e
e icien e que pe mi e el modelado y la simulación de los p ocesos de oma de
decisiones de los agen es en múl iples si uaciones en el dominio de espacios
in e io es. Ex iende las capacidades de Swee Home3D con cie os plugins
que pe mi an de ini el compo amien o de los agen es en el en o no de la si-
mulación. O as uncionalidades que o ece MASSIS son las isualizaciones
2D y 3D de la simulación, la capacidad de gua da las simulaciones pa a su
pos e io ep oducción y análisis.
Palab as cla e
•Modelado basado en agen es
•Modelo de decisión de agen es
•Simulación de mul i udes
•Escena ios de in e io es
•En o nos in eligen es
•F amewo k de simulación

Abs ac
Tes ing applica ions o sma en i onmen s is a di icul ask. I equi es
he ins alla ion o senso s and ac ua o s, he communica ions and he so -
wa e o he con ol sys em, and he pa icipa ion o people playing di e en
scena ios. This is cos ly, bo h in economic sense as well as in ime. Also,
he e a e some si ua ions ha canno be es ed o p ac ical easons (such
as eme gencies).
The use o simula ion ools ha p o ide some suppo o he de elop-
men o sma en i onmen applica ions is in e es ing, a leas o hese ea-
sons. One o he mos ele an aspec s o be conside ed in his kind o es s
is he human and social beha io o indi iduals when simula ing how peo-
ple in e ac wi h hei en i onmen , including o he indi iduals. I he simu-
la ion amewo k has o be used o di e en pu poses and by o he de elop-
e s, i should ha e a clea agen model, wi h some suppo o he design a
a highe le el o abs ac ion ha can be easily ansla ed o an implemen a-
ion.
This is he main pu pose o MASSIS (Mul i-agen sys em simula ion o
InDoo Scena ios), an e icien amewo k o modeling and simula ion o he
decision-making p ocess o agen s in mul iple si ua ions in indoo scena ios
domain. I ex ends he Swee Home3D en i onmen wi h plugins o linking
agen ’s beha io in he simula ion. O he unc ionali y p o ided by MASSIS
is he abili y o isualize he simula ion in 2D and 3D, and a ich log capa-
bili y, which can be he basis o u he analysis o he scena ios.
Keywo ds
•Agen -based modeling
•Agen decision model
•C owd simula ion
•Indoo scena ios
•Ambien In elligence
•Simula ion F amewo k
Table o Con en s
1 INTRODUCTION.......................................................................1
1.1 MOTIVATION OF THE WORK......................................................................................1
1.2 OBJECTIVES...........................................................................................................3
1.3 METHODOLOGICAL APPROACH..................................................................................4
1.3.1 Concep ion Phase............................................................................4
1.3.2 Agile De elopmen ...........................................................................4
Two E-mails pe day, mee ing whene e possible...................................................................4
Fas sp in s and sho i e a ions.............................................................................................5
1.4 DOCUMENT ORGANIZATION......................................................................................7
2 SIMULATION OF SMART ENVIRONMENTS.........................8
2.1 AGENT-BASED MODELING AND SIMULATION TOOLS........................................................9
2.2 SIMULATION OF INDOOR SCENARIOS.........................................................................11
2.3 CONCLUSIONS.......................................................................................................11
3 MASSIS OVERVIEW................................................................13
3.1 FRAMEWORK........................................................................................................14
3.1.1 Swee Home3D................................................................................14
3.1.2 MASON........................................................................................17
MASON Model.....................................................................................................................17
MASON Visualiza ion..........................................................................................................18
MASON U ili ies..................................................................................................................18
3.1.3 Pogamu 's POSH Engine................................................................19
3.1.4 Sa ing & Loading Simula ions.........................................................20
3.1.5 Visualiza ion..................................................................................21
3.1.6 S aigh Edge..................................................................................22
3.2 BUILDING REPRESENTATION....................................................................................23
3.3 PATH FINDING.....................................................................................................25
3.3.1 Visibili y G aph..............................................................................25
Obs acle me ging..................................................................................................................26
Obs acle la ening...............................................................................................................27
Using Swee Home3D ooms as sea ch a eas.........................................................................28
3.4 ELEMENTS LOCALIZATION......................................................................................29
3.4.1 Hyb id da a s uc u e: : A PR QuadT ee...........................................30
3.4.2 Bo om-Up P opaga ion QuadT ee Implemen a ion.............................32
Pa en nodes.........................................................................................................................32
Child nodes..........................................................................................................................33
Elemen – Lis node map.....................................................................................................33
Inse ing...............................................................................................................................34
Range Que ying...................................................................................................................34
VII
3.5 STEERING BEHAVIORS............................................................................................35
3.6 AGENT DECISION MODEL......................................................................................39
3.6.1 MASSIS' GOAP Beha io Model p o o ype.......................................40
Ac ion-Condi ion-E ec blocks.............................................................................................41
Beha io ...............................................................................................................................43
Beha io s ack......................................................................................................................43
Case s udy o his decision model : Kille – P ey................................................................44
3.6.2 MASSIS' beha io model: POSH Plans.............................................45
A lib a y o Beha io modules..............................................................................................46
POSH Dynamic ac ion selec ion sc ip s................................................................................48
D i e collec ions..............................................................................................................48
Compe ences...................................................................................................................49
Ac ion Pa e ns...............................................................................................................49
Case S udy o his decision model : Eme gency Simula ion................................................50
3.7 SAVING & LOADING SIMULATIONS...........................................................................53
3.7.1 Sa ing he simula ion.....................................................................54
Using SQLi e........................................................................................................................54
Using Plain Tex ..................................................................................................................54
Sa ing Disk Space................................................................................................................55
ZIP comp ession..............................................................................................................55
S ing subs i u ion...........................................................................................................55
Using concu ency o speeding up he p ocess....................................................................56
3.7.2 Simula ion Playback.......................................................................57
3.8 VISUALIZATION.....................................................................................................58
3.8.1 3D Visualiza ion............................................................................58
3.8.2 2D Visualiza ion............................................................................59
Laye -Based Display.............................................................................................................59
MASSIS' buil -in laye s........................................................................................................60
4 GETTING STARTED WITH MASSIS.....................................61
4.1 INSTALLATION.......................................................................................................61
4.1.1 Downloading MASSIS.....................................................................61
Windows...............................................................................................................................62
MAC OS X...........................................................................................................................62
Linux & Unix.......................................................................................................................62
4.2 ENVIRONMENT CREATION........................................................................................62
4.2.1 Launching he en i onmen edi o ....................................................62
4.2.2 Designing he en i onmen ..............................................................62
C ea ing & Edi ing walls......................................................................................................62
Adding doo s, windows & u ni u e......................................................................................63
Impo ing 3D objec s...........................................................................................................64
D awing ooms.....................................................................................................................64
Adding le els........................................................................................................................64
4.2.3 Me ada a Edi o .............................................................................65
4.2.4 Telepo Linking.............................................................................65
VIII
4.2.5 O he MASSIS' Design U ili ies.......................................................66
Designe ools.......................................................................................................................66
Name Gene a ion.................................................................................................................67
4.2.6 Final no es....................................................................................67
4.3 SPECIFYING BEHAVIORS WITH REACTIVE PLANS..........................................................68
4.3.1 Ins alling Ne beans and Pogamu 's yaPOSH edi o .............................68
Downloading & ins alling Ne beans......................................................................................68
YaPOSH edi o .....................................................................................................................68
4.3.2 POSH Plan c ea ion.......................................................................69
Basic Beha io s Design.........................................................................................................69
4.3.3 Linking wi h Swee Home 3D & MASSIS...........................................69
4.4 SIMULATION.........................................................................................................69
4.4.1 Running a new simula ion...............................................................69
4.4.2 Loading a sa ed simula ion..............................................................70
4.4.3 Help..............................................................................................70
5 CONCLUDING REMARKS.....................................................71
5.1 CONCLUSION........................................................................................................71
5.2 FUTURE WORK....................................................................................................72
In eg a ion o exis ing analysis ools....................................................................................72
Di e en AI beha io models................................................................................................72
Using a mo e powe ul 3D engine.........................................................................................72
Dis ibu ed Compu ing........................................................................................................72
6 REFERENCES..........................................................................73
7 APPENDICES.............................................................................I
IX
1 . In oduc ion
1.3 Me hodological App oach
A and science ha e hei mee ing poin in
me hod.
Edwa d G. Bulwe -Ly on
Fo he de elopmen o his p ojec , Agile so wa e de elopmen
p inciples we e ollowed. Due o he small size o he eam (1 pe -
son), and he na u e o his p ojec , an adap a ion o hese p inci-
ples was needed.
1.3.1 Concep ion Phase
The p ojec s a ed wi h an analysis phase. This phase was nec-
essa y because he eam had no domain-speci ic knowledge. A basic
unde s anding o he domain was needed in o de o be capable o
de e mine po en ial p oblems and needs. In his phase a e y sim-
ple p ocess was ollowed:
•Mee ing wi h he ad iso once a week.
•De ini ion o he goals ha should be accomplished o he
nex week.
•W i ing down new ideas, and de ining p ojec objec i es.
1.3.2 Agile De elopmen
The de elopmen o he p ojec s a ed on Oc obe , 2014. I was
decided o use a simpli ied agile me hodology, adap ed o ake in o
accoun ha he e we e only wo main pa icipan s: he s uden
and he ad iso . In o de o acili a e high in e ac ion be ween
hem, wo communica ion mechanisms we e used: emails and mee -
ings.
Two E-mails pe day, mee ing whene e possible
In o de o keep cons an communica ion be ween he s uden
and he ad iso , and moni o ing o p ojec p og ess, e e y day in
he mo ning, an e-mail was w i en o he ad iso , explaining he
expec ed goals o be accomplished ha day. A he end o he day,
4

1 . In oduc ion
an email con aining a b ie summa y o he achie emen s o he
day, he p oblems aced, and occasionally, a new idea o conside .
Face- o- ace communica ion is one o he pilla s o agile de elop-
men . Whene e he eam and he ad iso ound ime o mee , hey
me . The e was no a ixed day, bu usually was once a week.
Fas sp in s and sho i e a ions
Following Agile P inciples equi e equen deli e ies o p o o-
ypes o use ul p oduc s. Bu one o he main p oblems o a one
man eam is ha ew asks can be done in pa allel. Focusing on a
single sp in a a ime, and limi ing he amoun o wo k-in-p og ess
p o ides a be e p ojec de elopmen . Fo ha eason, he i e a-
ions we e composed o di e en sp in ypes, each one las ing one
week. Di e en ypes o sp in s we e designed:
•Li e a u e e iew sp in s: These sp in s we e in ended o
lea ning abou new amewo ks, eading and summa izing e-
sea ch pape s, in o de o make he eam capable o making
mo e di icul asks. Du ing his sp in s, i was e y common
he eme gence o new ideas. These new ideas we e classi ied
wi h a p io i y. These ypes o sp in s we e made only i he
esul s o he p e ious sp in s we e sa is ac o y.
•Implemen a ion sp in s: These sp in s ocused on he imple-
men a ion o he highes p io i y ea u e o implemen a he
momen . They we e ocused p ima ily on implemen ing he
asks de ined be o e.
•Tes ing sp in s: They we e ocused on es ing he unc ional-
i y o he sys em, bug ixing and pe o mance analysis.
•Sys em a chi ec u e e iew sp in s: A e implemen a ion
and es ing o he new ea u e/goal, he o e all sys em a chi-
ec u e is e isi ed, in o de o main ain cohe ence. The sys-
em a chi ec u e e iew sp in s had a leas one ace- o- ace
mee ing wi h he ad iso .
Figu e 1 shows he de elopmen wo k low o he i e a ions.
5
1 . In oduc ion
6
Figu e 1: De elopmen me hodology
A e he asks
o accomplishing
he goal known?
Highes p io i y
goal chosen
Conside lea ing i as
"Fu u e wo k"
Is i he cos
wo hwhile?
Upda e Goals & p io i ies
P o o ype
Is he pe o mance o
he new ea u e an
issue?
YES
NO
YES
Resea ch &
Es ima ion
Li e a u e e iew
Di ide goal
in o sub- asks
Has been
de eloped
a lib a y/ ool
al eady?
Is i Open sou ce?
Es ima e
In eg a ion
s
implemen a ion
om sc a ch
cos
Es ima e
implemen a ion
om sc a ch cos
W i e down
new ideas
o discussing
wi h he ad iso
NO
YES
YES
NO
NO
Implemen a ion
YES
Tes ing
Func ionali y es ing
pe o mance analysis
Sys em a chi ec u e e iew
pe o mance analysis
1 . In oduc ion
1.4 Documen O ganiza ion
The es o he documen is o ganized as ollows:
•Sec ion 2 e iews he s a e o he a , whe e he mos ele-
an aspec s, bo h heo e ical and echnological a e dis-
cussed. In i s i s sec ion, he main ools a e e iewed, along
wi h hei di e en cha ac e is ics. The second del es in o
he simula ion o indoo scena ios, highligh ing he mos im-
po an p oblems. Finally, he po en ial o mul i-agen ools
is shown, and he need o a ool like MASSIS is explained.
•Sec ion 3 In oduces he MASSIS (Mul i-agen Sys em Sim-
ula ion o InDoo Scena ios)
a chi ec u e and componen s. Special a en ion is gi en o
he de ini ion o he beha io o humans unde di e en si u-
a ions,
which includes he p ocess o decision making o he
agen s. O he ele an aspec s o model a e in e ac ions
among agen s and wi h hei en i onmen , he e en s on he
en i onmen , and he p ecise ep esen a ion o he building,
logging capabili ies and isualiza ion.
•Sec ion 4 Explains how o ge s a ed wi h MASSIS, he in-
s alla ion, en i onmen c ea ion, beha io modeling and sim-
ula ion.
•Sec ion 5 P esen s he conclusions and u u e wo k
7
2 . Simula ion O Sma En i onmen s
2 Simula ion O
Sma En i onmen s
The business o a is o e eal he ela ion be ween
man and his en i onmen .
D. H. Law ence
Sma En i onmen s[2] can be de ined as “a egion o he eal
wo ld ha is ex ensi ely equipped wi h senso s, ac ua o s and com-
pu ing componen s” [3]. Tha is, a sma en i onmen is awa e o
wha occu s wi hin i and i s su oundings, adap ing how i be-
ha es o achie e ce ain objec i es (e.g. making easie he li es o
he inhabi an s o a building). P e ious wo ks [4] ha e p oposed he
app oach o iewing sma homes as in elligen agen s, ha pe -
cei e hei en i onmen h ough he use o senso s, and can ac
upon he en i onmen h ough he use o ac ua o s, agen s ha
cons an ly adap hei beha io o he beha io o he en i onmen
i sel .
Besides o he sma en i onmen design, one o he main p ob-
lems ha a ises du ing he de elopmen o hese kind o en i on-
men s is hei he e ogenei y: he en i ies which inhabi he en i on-
men may be e y di e en o each o he , no known a p io i and
dynamic: may change o e ime.
Al hough he e ha e been se e al esea ches on his opic, such
as [5]–[7], es ing applica ions o sma en i onmen s con inues be-
ing a di icul ask. I equi es he ins alla ion o senso s and ac ua-
o s, he communica ions and he so wa e o he con ol sys em,
and he pa icipa ion o people who ha e o play he di e en sce-
na ios. This is cos ly, bo h in economic sense as well as in ime.
Also, he e a e some si ua ions ha canno be es ed o p ac ical
easons (e.g., a i e, people acciden s).
8
2 . Simula ion O Sma En i onmen s
Fu he mo e, om he poin o iew o he de elope s, who a e
used o i e a i e p ocesses, i is di icul o epea he es s i hey
ha e o pe o m hese wi h pe sons. A leas o hese easons is in-
e es ing o use simula ion ools ha p o ide some suppo o he
de elopmen o sma en i onmen applica ions. A ele an aspec
o be conside ed in his kind o es s is he modeling o he beha -
io o humans unde di e en si ua ions. The beha io o hese sce-
na ios equi es a leas he ollowing: in e ac ions among agen s,
wi h he en i onmen , and he p ocess o decision making.
Al hough many agen -based modeling and simula ion ools exis ,
amewo k, no speci ically o ien ed o sma en i onmen s.
2.1 Agen -based Modeling
And Simula ion Tools
Nowadays, he e a e many so wa e ools o implemen ing agen -
based models. Al hough many o hem a e o gene al pu pose, he
selec ion o one o ano he depends on many ac o s, such as he
scope and objec i e o he model o de elop, he execu ion pla -
o m, documen a ion and ease o use, o he eusabili y o he code.
One o he mos in luen ial so wa e packages, which has se ed
as inspi a ion o cu en pla o ms is Swa m [8], which began in
1994. The mos widely used pla o ms a e based on he Swa m phi-
losophy, which is o ien ed owa ds o he amewo k and lib a y
pa adigm. A amewo k ha de ines he concep s o agen -based
modeling, including also he lib a ies needed o implemen ing he
concep s p oposed acco ding o he amewo k.
In gene al, simula ion pla o ms ollow he OO pa adigm, whe e
he amewo k de ines he objec in cha ge o building and con ol-
ling he simula ion, as well as he objec s esponsible o he
g aphic elemen s managemen and he ep esen a ion o he execu-
ion esul s, along wi h he schedule sys em, which con ols he ex-
ecu ion o he e en s ha igge he me hods de ining he beha io
o he agen s.
9

2 . Simula ion O Sma En i onmen s
Repas [9] has been one o he Swa m based amewo ks has been
mo e success ul. I s a ed a he Uni e si y o Chicago, being o i-
en ed o he social sciences domain. The i s e sions o Repas a-
cili a ed he designing o models o use s wi hou an ex ensi e
knowledge o so wa e de elopmen , bu he undamen als o objec -
o ien ed p og amming, and knowledge abou he Ja a p og amming
language we e equi ed. Today, Repas con inues i s de elopmen a
A gonne Na ional Labo a o y. The la es e sion, Repas Sym-
phony [10], has e ol ed signi ican ly.
MASON[11], de eloped a Geo ge Mason Uni e si y, appea ed
sho ly a e Repas , as a compu a ionally e icien al e na i e. Al-
hough MASON is also based on he Swa m model, i sha es wi h
Repas many o aspec s men ioned be o e. MASON inc eases he
independence o he applica ion domain o he models and enhances
subs an ially hose cha ac e is ics necessa y in e y demanding
compu e models, such as ha dwa e independence, independence in-
e aces display and se ializa ion.
Ne logo [12] is ano he one o he mos widesp ead pla o ms. I
was de eloped a No hwes e n Uni e si y. Unlike Repas o MA-
SON, i is based on i s own high-le el language (based on Logo, a
Lisp dialec ). One o he di e ences ha Ne logo has o e o he li-
b a ies is ha Ne logo is well documen ed and i s lib a y has many
examples. Also, he p og amming s yle o Ne logo, which p o ides
many p imi i es and simple cons uc ion o g aphical in e aces,
makes easie he lea ning p ocess o he pla o m, especially o
use s wi hou deep aining in so wa e de elopmen . Al hough i
does no p esen he ad an ages o modula i y and euse o code
ha pla o ms based on mo e gene al-pu pose languages, he ease
o use and he abili y o gene a e and sha e he models as exe-
cu able apple s in any b owse ha Ne logo has, has made i one o
he ools mos used o simple models.
10
2 . Simula ion O Sma En i onmen s
2.2 Simula ion O Indoo
Scena ios
Se e al ools o simula ion and design o how people beha e in
indoo scena ios exis (many o hem comme cial), such as [13]–[18],
among many o he s. They ocus on scalabili y issues de i ed om
he managemen o a la ge numbe o agen s in eal ime, specially
when conside ing hei isualiza ion o he way he agen s ind hei
way while a oiding obs acles and o he agen s [19].
Specially in he las yea s, ocus has been on e y la ge numbe s
o agen s. Di e en echniques ha e been p oposed o cope wi h he
scalabili y issues, by elying on speci ic assump ions o he p oblem
unde s udy. This has an e ec on limi a ion o he lexibili y o
agen s' beha io , which is qui e homogeneous in mos o he cases.
Al hough hey a e app op ia e o simula e speci ic scena ios, i is
impo an o conside he human and social beha io o indi iduals
when simula ing how people in e ac wi h hei en i onmen , in-
cluding o he indi iduals. O he wo ks ha e be e add essed he
speci ica ion o he agen beha io , such as [1], [20]–[25].
Howe e , hey ha e no su icien ly aken in o accoun he
me hodological aspec s o a design p ocess when de eloping he
agen s' beha io . This is ele an when he simula ion amewo k
has o be used o di e en pu poses and by o he de elope s. In
hose cases, he e is a need o a clea e agen model, wi h some
suppo o he design a a highe le el o abs ac ion ha can be
easily ansla ed o an implemen a ion.
2.3 Conclusions
Agen -based modeling p o ides many ad an ages o e o he
modeling pa adigms. In gene al, he p ocess o abs ac ing he de-
ails o he a ge sys em and implemen ing hem unambiguously
on a compu e is much mo e di ec han o he me hods o abs ac-
11
2 . Simula ion O Sma En i onmen s
ion. The e o e, he model is mo e anspa en , which acili a es he
unde s anding o he hypo heses assumed and he inclusion o
knowledge o domain expe s.
The esul o his ease o abs ac ion is e lec ed in many ways.
Fo example, he a ie y in beha io o agen s is eno mous. Agen -
based modeling allows o conside he e ec o agen s ha ing lim-
i ed a ionali y, o agen s wi h abili y o lea n; om classic p oba-
bilis ic mechanisms (such as ein o cemen lea ning), o complex
models om cogni i e psychology (e.g endo semen sys ems). O
e en one s ep u he : The agen s could c ea e hei own models o
he wo ld hey pe cei e. The op ions a e almos limi less. The e-
laxa ion o he assump ions o ep esen a i e agen s, ha ing he
abili y o in e ac wi h he en i e popula ion, and an op imized be-
ha io o a u ili y unc ion ( e y common in many social sciences)
is done almos di ec ly using his pa adigm.
As ha e been s a ed in sec ions 2.1 and 2.2, he e is a gap be-
ween gene al pu pose lib a ies and specialized ools: Some a e oo
open, while o he s a e oo specialized, o hey do no ully in eg a e
design, modeling and simula ion. Taking his in o accoun , his
wo k p oposes an agen -based model o indoo scena ios whe e
bo h pe o mance and lexibili y in he beha io o he en i ies a e
sough . Agen s a e speci ied and managed indi idually, bu he e -
ec s o he c owd a e aken in o accoun by se e al me hods ha
ake ad an age o cha ac e is ics o he indoo domain in o de o
cope wi h he e iciency and scalabili y issues in he p ocessing o
hei mo emen s and hei isualiza ion. A he same ime, some al-
e na i e easoning mechanisms a e p o ided o each agen in o -
de o allow modeling o ich and he e ogeneous beha io s.
Ou app oach has been o adop an agen -based modeling ame-
wo k, Mason, and p o ide on his se e al componen s as a kind o
plugins ha acili a e he modeling and simula ion o indoo sce-
na ios. Mason has been chosen because i can be easily in eg a ed
as a Ja a lib a y, i clea ly decouples model om iews, i is ligh
and has an e icien schedule . The componen -based a chi ec u e o
Mason is shown in he nex chap e .
12
3 . MASSIS O e iew
3 MASSIS
O e iew
A complex sys em ha wo ks is in a iably ound o
ha e e ol ed om a simple sys em ha wo ked.
John Gall
MASSIS is a simula ion amewo k o scena ios in indoo en i-
onmen s, allowing o design spaces, and speci ying he beha io o
he elemen s and people in hem. These beha io s speci ica ions
may a y subs an ially: F om a simple p esence de ec o o human
beha io . I is capable o suppo ing housands o agen s, each one
wi h an speci ic beha io . The beha io speci ica ion is done ou side
he simula ion pla o m. Al hough cu en ly MASSIS p o ides only
one beha io model ( he POSH model, see sec ion 3.1.3), o he s can
be in eg a ed, bu his is le o u he s udy.
The simula ion p og ess can be isualized in 3D, om di e en
pe spec i es, o in 2D. The 2D isualiza ion lib a y is based on lay-
e s ( he elemen s o each laye a e d awn on op o he p e ious
laye ), making easie he de elopmen o a new ype o isualiza ion
o speci ic pu poses. Also, i allows o sa e he simula ion changes,
eco ding each agen s a e in e e y s ep o he simula ion. These
changes a e sa ed in an open and independen o ma (JSON), al-
lowing he analysis o he esul s om any o he pla o m and lan-
guage.
Bo h MASSIS and i s componen s a e open sou ce, allowing he
ex ension o i s unc ionali y by hi d pa ies.
13
3 . MASSIS O e iew
In addi ion o his, he se o plugins o Ne beans de eloped by
he Pogamu eam include a POSH plan edi o (Fig. 9). This plan
edi o helps wi h he AI de elopmen , allowing he pa allel de elop-
men o a highe -le el abs ac ion AI and p og am code.
3.1.4 Sa ing & Loading Simula ions
Also, he simula ion e en s can be logged in JSON o ma , as a
single zipped ile o in a SQLi e da abase o u he analysis.
Once a simula ion is pe o med, he expo ed da a can be used
o playback all e en s ha ha e occu ed du ing he execu ion o
he simula ion, i.e., he agen s will beha e in he same way hey did
du ing he simula ion.
20
Figu e 9: In eg a ed POSH Plan edi o in Ne beans
{
" eloci y": { "x": 32,"y": 58},
" isionRadio": 300,
"max o ce": 10,"maxspeed": 15,
"p ope ies": {"s ee ing.sepa a ion": 70,...},
"loca ionS a e":{
"angle": 0.7853982," loo Id": 8,
"cen e X": 4975.2285,"cen e Y": 4108.2695,...},
"id": 3673
}
Lis ing 1: Example o an agen 's sa ed s a e

3 . MASSIS O e iew
This is in e es ing o allow he use s o e iew he simula ion
when analyzing wha has happened.
3.1.5 Visualiza ion
All he changes made in he en i onmen a e e lec ed in eal
ime by 3D (Figu e 10 ) and 2D displays (Figu e 11).
Al hough 3D display is mo e ealis ic, he 2D iew is use ul o
analysis and debugging. Also, he 2D isualiza ion API allows he
c ea ion o use -de ined laye s in o de o il e he di e en ele-
men s in ol ed in he simula ion.
21
Figu e 10: Simula ion 3D iew
Figu e 11: Simula ion 2D iew example : C owd Densi y
3 . MASSIS O e iew
3.1.6 S aigh Edge
O he open sou ce lib a ies ha a e used in MASSIS, S aigh -
Edge[32] dese es an special men ion. I is a powe ul polygon li-
b a y, ha p o ides g ea unc ionali y o MASSIS engine.
I p o ides se e al geome ic u ili ies, pa h- inding , ield o i-
sion and u ili y classes o con e ing S aigh Edge polygons o
Ja a Topology Sui e (JTS)[33] polygons, allowing o pe o m mo e
complex geome ic ope a ions, such as polygon la ening and
sh inking, in e sec ions and unions.
Se e al modules o S aigh Edge we e ex ended in o de o im-
p o e hei e iciency (Pa h inding) o o add new unc ionali ies
(e.g. ec o ope a ions o Swee Home3D polygon ans o ma ions).
22
3 . MASSIS O e iew
3.2 Building Rep esen a ion
E e ybody a he pa y is a many sided polygon.
Nonagon, They Migh Be Gian s.
How he en i onmen is ep esen ed is one o he key elemen s in
any simula ion amewo k, because his ep esen a ion is he basis
o all he simula ion sys em. I is e y impo an o choose an ap-
p op ia e way o model he en i onmen depending on he domain.
Fo example, an en i onmen can be modeled as a con inuous
space, a g id, o a g aph. O e en a combina ion o hese. The un-
de lying s uc u es chosen o model he en i onmen s depend on
he le el o desi ed in he applica ion.
I is e y common in he indoo scena io domain ha elemen s
a e e y close om each o he . Agen s a e no he only ones consid-
e ed he e; ables, chai s, cabine s, sinks, e en lowe po s o adia-
o s.
The e o e, MASSIS op s o a ep esen a ion o he building in
which he space is no disc e ized in cells ha a e occupied by one
23
Figu e 12: In MASSIS, almos e e y elemen is a polygon.
3 . MASSIS O e iew
elemen o ano he . Ins ead, each elemen has a posi ion in h ee
dimensions: (x, y, building- loo ), and a polygon ha ep esen s i .
This can be seen in Figu e 12 , whe e e e y hing is ep esen ed
in a polygonal way: agen s (g een a ows enclosed by ci cles), doo s
( hin g een ec angles), walls (black ec angles), ooms (g ay a eas),
s ai s,(con iguous ed and g een ec angles), ision a eas,e c.
An example ha illus a es his need o a model o his ype a e
doo s. The wid h o he doo s is a c ucial ac o in de e mining se -
e al aspec s o he model, such as c owd conges ion o pa h inding.
The disc e iza ion le el necessa y o achie e he same esul s using
uni o m g ids in a building wi h se e al loo s wi h housands o
agen s is somewha p ohibi i e.
As i will be explained in subsequen sec ions, he use o poin s
wi h pa icula da a s uc u es o manage hem e icien ly, a he
han uni o m g ids has many ad an ages. One o hem is shown in
Figu es 13 and 14 .
24
Figu e 13: Mo emen in a con inuous space model
Figu e 14: Mo emen in a low- esolu ion g id
In indoo scena ios, he use o a low- esolu ion g id may cause s ange beha io s
X
3 . MASSIS O e iew
3.3 Pa h Finding
I you ind a pa h wi h no obs acles, i p obably
doesn' lead anywhe e.
F ank A. Cla k
Pa h inding is one o he issues ha has mo e impac when simu-
la ing c owd beha io s. Some models ea he c owd as a single en-
i y o a g oup in o de o simpli y he numbe o calcula ions, such
as in [34]–[36]. Howe e , MASSIS, as i has been s a ed in he in-
oduc ion, has as objec i e o suppo lexibili y in agen beha io ,
he e o e he pa h inding model is implemen ed indi idually o
each agen , bu aking ad an ages o some assump ions om he
p oblem domain in o de o gain in e iciency.
As i is explained in p e ious sec ions, he building model is ep-
esen ed on a con inuous space. The e o e, i makes sense o ollow
his kind o design when de eloping a pa h inding module. I obs a-
cles a e ep esen ed as polygons, building a pa h h ough a isibil-
i y g aph is a good app oach.
3.3.1 Visibili y G aph
A Visibili y G aph is a g aph whose nodes co espond o geome -
ic componen s, such as e ices o edges, and he nodes o his
g aph a e connec ed only i he e is no any obs acle in e sec ing
he segmen be ween hose wo poin s. Each edge o his g aph ep-
esen s a isible connec ion be ween hose wo poin s (They can see
each o he ). In Figu e 26 shows an example o a isibili y g aph.
25

3 . MASSIS O e iew
As all he elemen s p esen in he simula ion ha e a polygon as-
socia ed o hem, his ep esen a ion o he elemen s is e y handy
o he obs acles p ep ocessing s ep. This p ep ocessing s ep is done
be o e he simula ion s a s, in o de o gain compu a ional speed
du ing he simula ion.
The main eason o his p ep ocessing s ep is ha some compo-
nen s o he building, such as walls, o s ai s, a e ne e going o
mo e, so he e's no eason o ecompu e he isibili y g aph o he
building e e y ime a pa h is eques ed. The p ep ocessing s ep
consis s basically o he ollowing pa s:
Obs acle me ging
As MASSIS is in ended o indoo scena ios simula ions, some
cha ac e is ics o his domain a e exploi ed, such as he high e-
quency o wall in e sec ion.
Usually he end o a wall is connec ed wi h ano he . In Swee -
Home 3D ep esen a ions, each wall is conside ed as an indi idual
polygon. This is e y use ul o he building design s age, bu when
he numbe o polygons becomes impo an , i could become a
p oblem. The way he walls in e sec makes possible he educ ion
o he numbe o edges. (See Fig.16 )
26
Figu e 15: Visibili y g aph
3 . MASSIS O e iew
Obs acle la ening
The geome y o he obs acles is expanded by an amoun p opo -
ional o he bounding adius o he agen s. This p ocess is done in
o de o make gene a ed pa hs mo e ealis ic, because he pa h is
gene a ed wi h he e ices o he expanded obs acle, no om he
obs acle i sel , so he pa h ha he agen ies o ollow is sligh ly
sepa a ed om he obs acles e ices and edges. Fig. 17, shows an
example o how an agen (blue smiley) should ollow he gene a ed
pa h. Also, his ope a ion helps o ob ain as e he isible edges
om any poin , as will be shown in subsequen sec ions.
27
Figu e 16: Reduc ion o numbe o edges.
Figu e 17: Expanded obs acle polygon
3 . MASSIS O e iew
Using Swee Home3D ooms as sea ch a eas
One o he mos basic pa s o he building designing wi h
Swee Home3D is oom c ea ion. The p og am p o ides a simple
way o naming a eas and colo ing he loo . MASSIS akes ad an-
age o his way ha Swee Home3D has o ep esen ing he build-
ing o speeding up he algo i hm. Assuming simple s a emen s,
such as:
1. The agen is always inside a oom.
2. The a ge (goal) is always inside a oom.
3. A pa h om Room A o Room B (being A and B di e en )
mus c oss a leas a doo .
Al hough hese assump ions seem qui e ob ious, hey make a g ea
imp o emen in he execu ion speed o he algo i hm. As he obs a-
cle polygons a e in la ed, hei accessible e ices om he inside lie
now in he in e io o he oom shape.
The mos expensi e ope a ion in e ms o compu a ion ime is he
inding o he isible nodes ( e ices) om he s a and he goal o
he pa h, because he es o he isible connec ions be ween e -
ices is p ecalcula ed in ad ance, and he pa h inding be ween wo
nodes in a p ecompu ed g aph is done e y quickly.
Fo inc easing he algo i hm execu ion speed, he only nodes o be
aken in o accoun as s a ing / ending nodes a e he ones inside
he oom bounda ies con aining he s a poin and he end poin .
In his way, mos o he nodes and line segmen s a e disca ded,
boos ing he algo i hm speed.
28
3 . MASSIS O e iew
3.4 Elemen s Localiza ion
The e's nowhe e you can be ha isn' whe e you' e
mean o be
John Lennon.
People, senso s and ac ua o s need eal ime in o ma ion abou
he elemen s ha su ound hem. This implies ha , du ing simula-
ion, lo s o que y anges mus be pe o med. I is ob ious ha i e -
a ing o e e e y elemen on a loo checking dis ances and in e sec-
ions is no a good idea. So, he e is a need o a da a s uc u e ca-
pable o holding he simula ion elemen s, being somewha e icien
in da a inse ion, and e ie al.
An in ui i e way o s o ing he loca ions o he elemen s in-
ol ed du ing he simula ion is using uni o m g ids. Howe e , hese
ypes o da a s uc u es a e use ul when he spa ial da a is dis ib-
u ed in an homogeneous way, which is a ely seen in MASSIS' sim-
ula ions. Also, depending on he equi ed accu acy, hey can con-
sume oo many esou ces.
Using uni o m g ids wi h an app op ia e cell size can be a good
app oach o he p oblem. Bu de e mining he cell size is no an
easy ask, and i depends on many ac o s. I hey a e small, he e
will be only a ew elemen s pe cell bu many cells o check. The
opposi e case is analogous. I he cell size is la ge, ew cells mus be
checked bu he e would be many elemen s pe cell.
Ano he solu ion could be using QuadT ees [37]. QuadT ees a e
a iable esolu ion da a s uc u es ha could be used o e ie e
he agen 's neighbo s wi hin a adius in an e icien manne . This
da a s uc u e is use ul o que ying anges, bu i does no pe o m
as well as a uni o m g id when inse ing and dele ing elemen s. Fig-
u e 18 shows an example o space pa i ion using a QuadT ee (Only
hal o i is shown).
Gi en hese ac s, he ques ion o which o he wo da a s uc-
u es is bes o MASSIS a ises. Bo h o hem ha e hei espec i e
ad an ages and disad an ages. Uni o m g ids a e c ea ed once,
29
3 . MASSIS O e iew
Fig 23 illus a es hese de ini ions wi h some examples (Seek and
Flee, obs acle a oidance and Pa h Following).
MASSIS p o ides a lexible implemen a ion o se e al beha io s
o his ype(e.g. seek, a i al, sepa a ion, collision a oidance, wall
con ainmen , and pa h ollowing), ha can be g ouped in o mo e
complex beha io s (like locking o queuing, o example). Al hough
he e a e se e al s ee ing beha io s implemen ed in MASSIS, o il-
lus a ion pu poses, one a ian o he collision a oidance be ween
agen s used in MASSIS will be desc ibed.
In his example, he ini ial posi ions o wo agen s, a and b a e
de ined by hei x,y coo dina es;
(xa1, ya1)
and
(xb1, yb1)
. I he e
is a collision, he poin whe e each agen collides wi h he o he one
is de ined by
(xa2, ya2)
and
(xb2, yb2)
(See Fig. 24). The e exis s a
ela ion be ween hem, de ined by:
36
Figu e 23: Some s ee ing beha io s:
Seek and Flee, obs acle a oidance and Pa h Following
Figu e 24: Collision a oidance be ween wo mo ing agen s

3 . MASSIS O e iew
Whe e is he ime ha mus pass be o e he collision occu s.
The dis ance be ween hem should be g ea e han he sum o hei
adius.
In he ime , he dis ance be ween he wo elemen s will be
Sol ing o , in he abo e equa ions, wo alues a e ob ained:
1
and
2
(
2
is analogous o
1
):
The lowes alue o should be chosen, in o de o ob ain he nea -
es poin o he u u e collision. An special case mus be conside ed:
i he alue o
1
o
2
is nega i e, mus be disca ded (The
agen s a e always mo ing o wa d). This is illus a ed in Figu e 25.
The pseudo-code o his algo i hm is shown in Lis ing 2.
37
3 . MASSIS O e iew
38
Figu e 25: Region di ision done by he wo solu ions o
unc ion collisionA oidance e u ns epulsionVec o
minT = In ini y;
o Agen o he in ge Agen sInRange(agen )
dis ance = dis ance(o he ,agen )
d = agen . adius + o he . adius
= ge TimeToCollision(agen , o he , d);
i > 0 and < minT
minT =
u u eA = u u eLoca ion(agen , )
u u eO = u u eLoca ion(o he , )
epulsionVec o = no malize( u u eA – u u eO)
Lis ing 2: MASSIS' Collision A oidance pseudo code
3 . MASSIS O e iew
3.6 Agen Decision Model
Desi es dic a e ou p io i ies, p io i ies shape ou
choices, and choices de e mine ou ac ions.
Dallin H. Oaks
The aspec s o human beha io ha a e o in e es o modeling
indoo scena ios in he MASSIS amewo k a e he mechanisms
ha humans use o deal wi h p oblems easoning om con ex ,
making use o collec i e in elligence, and how his in elligence is
used in p oblem sol ing.
Fo ha eason, each agen in MASSIS mus ha e i s own beha -
io , compu ed by some high-le el decision model and execu ed by a
low low le el module (speed, posi ion, angles, e c.).
Modeling human beha io wi h agen s ha e o conside wo main
aspec s: hose ela ed wi h hei pe cep ion and he in e ac ion
wi h he en i onmen , and hose dealing wi h he easoning on he
con ex and he decision making. They a e implemen ed as low-
le el and high-le el beha io componen s, espec i ely. When he
high le el componen decides wha o do nex , he ac ion is exe-
cu ed by he low-le el componen , which pe o ms all he necessa y
ope a ions. The ela ionship be ween hese componen s is illus-
a ed in Fig. 26.
39
Figu e 26: Rela ionship be ween high-le el and low-le el modules
HIGH LEVEL
Reac i e Plans
P imi i es (Ac ions, Senses)
LOW LEVEL
Posi ion,
line o sigh ,
speed, angle,
o ce, mass
S ee ingPa h inding
Pe cep ion
Mo ionLocomo ion
Pa h ollowing,
alignmen ,
queuing,sepa a ion,
collision a oidance...
3 . MASSIS O e iew
Bo h high-le el and low-le el beha io s should be a ec ed by he
s a e o he agen , al e ing he decision making p ocess (e.g. a
sca ed agen may choose a di e en ou e o each a a ge , p oba-
bly a longe one).
Du ing he de elopmen o MASSIS, se e al ways o implemen -
ing agen 's high-le el beha io we e conside ed, and some p o o-
ypes we e made in o de o es which o he di e en easoning
me hods would i be e in his p ojec . I is ob ious ha he e is
no clea winne in his a ea, so MASSIS in as uc u e was designed
in o de o allow an easy in eg a ion o di e en ypes o AI mod-
ules.
Due o ime cons ain s, only one AI module has been ully im-
plemen ed, bu a ious p o o ypes based on di e en echniques
we e de eloped, such as Goal O ien ed Ac ion Planning. This las
one dese es o be explained; i mo e ime and esou ces we e a ail-
able, i would ha e been eleased wi h he MASSIS inal e sion.
3.6.1 MASSIS' GOAP Beha io Model
p o o ype
GOAP (Goal O ien ed Ac ion Planning)[39], [40] p oposes a way
o ac ion planning in eal ime o NPCs (Non-Playe Cha ac e s)
in ideogames. I was used i s ly in F.E.A.R[41], an AAA i s pe -
son shoo e eleased o PC in 2005.
The planning sys em i 's based pa ially on he STRIPS Plan-
ning [42], consis ing on goals and ac ions. Goals desc ibe some de-
si ed s a e o he wo ld, and ac ions a e de ined in e ms o p econ-
di ions and e ec s.
The ac ions only can be execu ed i all i s p econdi ions ha e
been me , and e e y ac ion changes he wo ld in one way o an-
o he .
The way o linking p econdi ions, ac ions and e ec s is done us-
ing A*, whe e he wo ld s a es a e he nodes o he g aph and edges
a e he ac ions o each hem.
The MASSIS GOAP P o o ype was based in his idea. The main
concep s a e he ollowing:
40
3 . MASSIS O e iew
1. (P e)Condi ion: Ci cums ance necessa y and indispensable
o an ac ion o be ca ied ou .
2. Ac ion: “Do some hing”. I equi es ce ain p econdi ions o
be execu ed, and causes e ec s.
3. E ec : Impac o consequence o an ac ion. I Adds o
changes he eal o pe cei ed wo ld.
4. Wo ld: The building.
5. Pe cep ion (Sense): In o ma ion managed by he agen , in
ela ion wi h i s en i onmen . The e a e wo ypes o Senses:
Real / Ex e nal senses and In e nal senses.
◦Real / Ex e nal senses: They a e he eal wo ld ac s
pe cei ed om he agen . Fo example:
▪“The e a e 5 i ems on ha able”
▪“The e is a i e on his oom”
▪“Somebody is looking a me”
◦In e nal senses: They a e he hings pe cei ed om
he in e nal wo ld o he agen . In e nal senses a e he
esul o p ocessing he Ex e nal senses in o some hing
ha exis s only in he agen 's mind. Fo example:
▪“The e a e a lo o i ems on ha able”
▪“I am no com o able in his oom”
▪“Tha pe son makes me ne ous”
The p econdi ions a e igh ly coupled wi h pe cep ions, being in
mos cases he same.
Ac ion-Condi ion-E ec blocks
Di e en ac ions can p o oke he same e ec (s), bu di e en
p econdi ions.
Fo example, i he goal is o ha e a com o able chai , di e en
ac ions can achie e his goal:
Ac ion: Buy a chai (we omi he going- o-Ikea pa )
Condi ion: Ha e money.
E ec s:
Ha e a com o able chai .
Ha e less money
Ac ion: Buy ools o making chai s.
41

3 . MASSIS O e iew
Condi ion: Ha e money.
E ec :
Ha e ools o making chai s.
Ha e less money.
Ac ion: Bo ow ools o making chai s om a iend.
Condi ion: Ha e iends wi h ools o his ype.
E ec :
Ha e ools o making chai s.
And much mo e o hese Ac ion-Condi ion-E ec blocks should
be necessa y o gi ing he agen he abili y o making a decen
plan.
Depending on he agen 's pe cep ion o he wo ld, his blocks a e
linked oge he dynamically, and a g aph sea ch is pe o med on he
42
Figu e 27: Joined Ac ion-Condi ion-E ec blocks
Ha e a a he
wi h lo s o money.
Ha e low mo al
s anda ds
AND
Be a no -so-good pe son
Ask Dad o money
Ha e Money
AND
S eal o he people
Buy wood
Ha e Wood
C ea e a chai
Ha e a saw
AND
Ha e a chai
Ha e iends
owning wood-wo king ools
Ask a iend
o wood-wo king ools
Buy
wood-wo king
ools
Be willing o wo k
AND
Ha e a job
wo k ha d
Pe sonal
sa is ac ion
Buy a chai
3 . MASSIS O e iew
Ac ion-Condi ion-E ec g aph. Figu e 27 shows a simple example o
joining he p econdi ions wi h he ac ions and e ec s.
Beha io
In his con ex , a beha io is a se o ac ions ha mus be pe -
o med (o de ed o no ) in o de o each a goal. Basically, i con-
sis s on some condi ions, which “ i e” i s ac i a ion and o he con-
di ions which in alida es hem.
Beha io s a e o de ed by p io i ies: Some e en s in he wo ld
change he way we beha e: Fo mos people, seeing a dinosau com-
ing changes hei sho - e m p io i ies. An example o beha io p i-
o i ies is illus a ed in Figu e 28.
Beha io s ack
When a beha io becomes in alida ed by some e en , he agen
mus con inue doing wha e e i was doing. Tha is he eason why
e en s in he en i onmen do no eplace he cu en beha io , hey
add i o a beha io s ack.
The agen always has on he bo om o i s beha io s ack he
lowes - p io i y beha io . When an e en o ces he agen o change
i , a new beha io is added on he op o he s ack. I a i ing condi-
ion o a lowe le el beha io is sa is ied, ha beha io is no added
o he s ack; i has a lowe p io i y and shouldn' be aken in o ac-
coun . Figu e 29 illus a es an example o he managemen o a be-
ha io s ack depending in he en i onmen .
43
Figu e 28: Beha io p io i ies in MASSIS' GOAP Va ian
Flee om i e beha io
See i e
Be sa e
Flee om i e
Plan execu ion o each
main goal
Ge ou o he building beha io
Being sa e
Be ee
Ge ou o
he building
Plan execu ion o each
main goal
Escape om dinosau beha io
See a dinosau
Be sa e
Flee om dinosau
Plan execu ion o each
main goal
P io i y #1P io i y #2P io i y #3
3 . MASSIS O e iew
Case s udy o his decision model : Kille – P ey
Fo es ing his decision model, a Kille – P ey scena io was
modeled. In his scena io, h ee oles we e p esen :
1. Kille : Wan s o kill e e y agen ma ked as P ey, bu o
killing a p ey a Kni e is needed. I i does no see any p ey
agen , wande s a ound.
2. P ey: Wande s a ound un il i sees a Kille . When a Kille
i s seen by a P ey, his las one lees as as as possible.
3. Kni e: A simple kni e. Can be aken by a Kille , and hey
a e dis ibu ed h ough he building. Fo making he simula-
ion mo e in e es ing, a kni e can be used only once.
44
Figu e 29: Beha io s ack changes due o e en s in he en i onmen
Beha io s ack
Ge ou o he building
Fi e appea s
Beha io s ack
INSERT [Escape om i e]
Ge ou o he building
A dinosau is seen
Beha io s ack
INSERT [Flee om dinosau ]
Escape om i e
Ge ou o he building
Dinosau slips and
alls om a window
Beha io s ack
INVALIDATE [Flee om dinosau ]
Escape om i e
Ge ou o he building
Beha io s ack
Escape om i e
Ge ou o he building
Suppose ha he
"Escape om i e"
beha io does some hing
o ex inguish he i e
Beha io s ack
INVALIDATE[Escape om i e]
Ge ou o he building
Figu e 30: Kille – P ey P o o ype sc eensho
3 . MASSIS O e iew
Lis ing 3 shows pa o he ace o he simula ion, and Figu e 30
shows an sc eensho o i . The ed agen is he kille , he yellow
ones he p eys, and he blue do s a e kni es.
3.6.2 MASSIS' beha io model: POSH Plans
While he me hod desc ibed abo e looks p omising, he de elop-
men and es ing o i would ha e aken oo much ime. The e o e,
i was decided o use a mo e ma u e beha io model, well known
and es ed: POSH[43] (Pa allel- oo ed, O de ed Slip-s ack Hie a -
chical) dynamic plans.
The a chi ec u e o his beha io ollows he BOD (Beha io O i-
en ed Design) me hod[43], [44]. This me hod o building agen s
combines he ad an ages o Beha io Based AI[45], [46] and objec -
o ien ed design app oaches.
In MASSIS his is applied o acili a e he design o agen s ha
a e capable o unning in pa allel and o gene a ing a beha io ha
can sa is y mul iple objec i es ha may con lic wi h each o he .
One o he main issues while designing an au onomous agen is
ha many o he goals ha he agen wan s o be accomplished
45
============Begin Planning============
Ini ial s a e : {isP eyKilled=0.0, Numbe O KilledPeople=0.0, Numbe O Kni esVisible=0.0,
isP eyVisible=0.0, Numbe O P eysVisible=0.0, isKni eVisible=0.0, isAli e=1.0, isPe son=1.0}
A ailable Ac ions : [TakeKni e, Wande A ound, KillP eyAc ion, Sea chKni e, Sea chP ey]
Planning o goal [Wande Finished:==1.0]
--------------------------------------
Ac ions : [Wande A ound]
============End Planning============
[STATE]Execu ing Ac ion
The pe cei ed en i onmen has changed, o cing a change on he agen beha iou .
Fi ing condi ion: [isP eyVisible:==1.0] /* A p ey was seen */
Cu en beha iou : Beha iou [Kill P ey Beha iou ]
Beha iou s ack: [Beha iou [Wande a ound], Beha iou [Kill P ey Beha iou ]] /* Beha io added o
he s ack */
[STATE]idle - Replanning nex goal
============Begin Planning============
Ini ial s a e : {isP eyKilled=0.0, Numbe O KilledPeople=0.0, Numbe O Kni esVisible=0.0,
isP eyVisible=1.0, Numbe O P eysVisible=1.0, isKni eVisible=0.0, isAli e=1.0, isPe son=1.0}
A ailable Ac ions : [TakeKni e, Wande A ound, KillP eyAc ion, Sea chKni e, Sea chP ey]
Planning o goal [isP eyKilled:==1.0]
--------------------------------------
Ac ions : [Sea chKni e, TakeKni e, Sea chP ey, KillP eyAc ion]
============End Planning============
[STATE]Execu ing Ac ion
Ac ion Sea chKni e Finished.
Wo ldS a e : {isP eyKilled=0.0, Numbe O KilledPeople=0.0, Numbe O Kni esVisible=1.0,
isP eyVisible=1.0, Numbe O P eysVisible=3.0, isKni eVisible=1.0, isAli e=1.0, isPe son=1.0}
[STATE]Execu ing Ac ion
Ac ion Sea chKni e Finished
[STATE]Execu ing Ac ion
[STATE]Execu ing Ac ion
Ac ion TakeKni e Finished
Ac ion Sea chP ey Finished.
Wo ldS a e : {isP eyKilled=0.0, Numbe O KilledPeople=0.0, HasKni e=1.0, Numbe O Kni esVisible=1.0,
isP eyVisible=1.0, Numbe O P eysVisible=1.0, isKni eVisible=1.0, isAli e=1.0, isPe son=1.0}
[STATE]Execu ing Ac ion
Ac ion Sea chP ey Finished
[STATE]Execu ing Ac ion /* P ey was s abbed */
The pe cei ed en i onmen has changed making he ac ual beha iou (Beha iou [Kill P ey Beha iou ])
in alid. /*P ey is now dead and he e is no any o he isible*/
[STATE]idle - Replanning nex goal
Lis ing 3: MASSIS' GOAP Case s udy ace
3 . MASSIS O e iew
52
Figu e 36: Teache going o he doo
Figu e 38: Teache going o ake
he nea es chai
Figu e 39: The s uden s ollow he
eache o escaping om he
building.
Figu e 37: The s uden s p oceeding
o close he windows.

3 . MASSIS O e iew
3.7 Sa ing & Loading
Simula ions
I hink you can lea n om his o y.
Chuck No is
Al hough MASON p o ides a way o sa ing simula ion s a es, i
does no p o ide a way o s o ing some kind o eco d o he en i e
simula ion. Tha 's he eason why a new module o sa ing and
loading he simula ion esul s was de eloped. Two ways o sa ing
simula ions we e conside ed when de eloping MASSIS: Ja a Se ial-
iza ion o ma and a plain- ex o ma .
•Ja a se ializa ion:
◦Good poin s:
▪Easy implemen a ion
▪I i is done ca e ully, he disk space used is ela i ely
small.
◦Bad poin s:
▪I has high dependence om he codebase
▪Makes e y di icul analyzing he simula ion esul s
om o he pla o m/language.
•Plain ex :
◦Good poin s:
▪I can be pa sed by any pla o m and language
▪I is human- eadable
◦Bad poin s:
▪Can use high amoun s o disk space
▪A cus om pa se is needed, and also an speci ica ion o
he ile o ma .
▪Making changes in he codebase may imply ew i ing
oo much code, i e e y hing is done “by hand”.
53
3 . MASSIS O e iew
Balancing he ad an ages and disad an ages o hese wo ways
o s o ing he simula ion imeline, he second app oach (Plain –
Tex ) was chosen, bu using JSON (Ja aSc ip Simple Objec No a-
ion)[47] as ile o ma . JSON is a well known ex o ma , sui able
o s o ing objec de ini ions. Fo logging he simula ion s a e o
his o ma , an ex ension o he well-known Gson [48] lib a y, Gson
on Fi e [49] was used. I se ializes he objec a ibu es in o JSON
o ma , and also p o ides se e al u ili ies o sol ing ypical issues
when doing hese hings (e.g. ci cula e e ences).
3.7.1 Sa ing he simula ion
The simula ion changes can be sa ed di ec ly in a (zipped) ex
ile, o in a SQLi e [50] da abase.
Using SQLi e
SQLi e was chosen because is a e y po able da abase o ma .
The da abase con ains only 3 ields: <objec -id>,<s ep-id>,<ob-
jec -s a e>. <objec -id> i 's he simula ion objec unique id,
<s ep-id> he ime-s ep o he simula ion and <objec -s a e> is
he ac ual s a e o he simula ion objec . The main ad an age o
his way o s o ing he simula ion esul s is ha making a simple
selec s a emen , all he changes ha he agen made du ing simu-
la ion can be e ie ed (See Lis ing 7).
Al hough his is no a bad app oach, he numbe o eco ds in
he able g ows e y quickly, and i s ile size has an ex a o e head
ega ding he pu e-plain- ex app oach.
Using Plain Tex
Much mo e simple han he SQLi e op ion: one line pe s ep,
con aining he elemen s ha changed in ha s ep. Ve y as o
54
selec simula ion_objec _id,
max(s ep_id),
simula ion_objec _s a e
om simula ion_log
whe e s ep_id < DESIRED_STEP
g oup by simula ion_objec _id
Lis ing 7: SQLi e que y o e ie e he changes made by an elemen o he
simula ion.
3 . MASSIS O e iew
w i e and o playback o wa d (bu no backwa ds, ha op ion is
be e wi h he SQLi e app oach).
Sa ing Disk Space
JSON is a compac ex o ma (compa ed wi h XML). The ile
size can be educed conside ably, i some me hods a e applied.
ZIP comp ession
Applying ZIP comp ession o he simula ion esul s educes no-
o iously i s ile size: up o 75%. I he simula ion esul s a e sa ed
in o a SQLi e da abase, he da abase mus be comp essed a e , bu
i hey a e sa ed di ec ly in o a ile, he s eam can be comp essed
“on he ly” (Tha is, less space consumed o e all).
S ing subs i u ion
Al hough he ZIP comp ession achie es good esul s, some op i-
miza ions can be made in o de o dec ease e en mo e he ile size.
Lis ing 8 shows an example o an agen s a e in a conc e e simula-
ion s ep.
Blue s ings a e epea ed wi h a e y high equency ( hey a e
objec a ibu es, o class names). A ibu es like “ eloci y” a e
common o e e y mo emen -capable agen , e.g. a pe son. I he 8
55
{
" eloci y": { "x": 1, "y": 1 },
" isionRadio": 300,
"max o ce": 10,
"maxspeed": 15,
"p ope ies": {
"s ee ing.alignmen ": 5,
"isP ey": 1,
"s ee ing.sepa a ion": 70,
"s ee ing. ollowpa h": 25,
//...
},
"loca ionS a e": {
"angle": 0.7853982,
" loo Id": 8,
"cen e X": 4975.2285,
"cen e Y": 4108.2695,
" ype": "SimLoca ionS a e"
},
"id": 3673,
" ype": "Agen ClassNameS a e"
}
Lis ing 8: Un-comp essed agen s a e in JSON o ma .
3 . MASSIS O e iew
cha ac e s ha “ eloci y” has can be subs i u ed by some hing
sho e , he o e all size o he simula ion esul s ile would be con-
side ably smalle .
The keys ( ep esen ing objec a ibu es), and equen s ing
alues (such as class names) can be eplaced by a sho e s ing,
some hing like a numbe p eceded by an special cha ac e ha
se es as indica o , like “@”. Bu , why a numbe ? Ins ead o a num-
be can be an alphanume ic cha ac e , so, ins ead o wo king in
base 10, we a e wo king in base 62. Lis ing 9 shows how he agen
s a e is ep esen ed a e his “ educ ion” p ocess.
I is ob ious ha some kind o ex a in o ma ion mus be s o ed
in o de o be capable o e e ing he p ocess. So, a mapping a ay
mus be sa ed oge he wi h he simula ion esul s. An example o
his mapping a ay is shown in Lis ing 10.
Using concu ency o speeding up he p ocess
I/O ope a ions a e cos ly. As e e y simula ion s ep implies w i -
ing o disk (i sa ing is enabled), he execu ion speed o he simula-
ion can be educed signi ican ly.
MASSIS uses an ex a h ead o w i ing his in o ma ion. A
synch onized queue is used as w i ing bu e . E e y s ep, he in o -
56
{
"@D": { "x": 1, "y": 1 },
"@E": 300, "@F": 10, "@G": 15,
"@H": { "@6": 5,"@7": 1,"@8": 70,"@9": 2, "@A": 25,...},
"@I": {
"@0": 0.7853982, "@1": 8, "@2": 4975.2285, "@3": 4108.2695,
"@5": "@4"
},
"id": 3673, "@5": "@J"
}
Lis ing 9: S ing subs i u ion in an agen 's sa ed s a e.
[
["@0","angle"],["@1"," loo Id"],
["@2","cen e X"],["@3","cen e Y"],
…
]
Lis ing 10: A ay mapping key alias wi h ac ual keys
3 . MASSIS O e iew
ma ion abou he changes is pushed in o he queue. The w i ing
h ead is con inuously aking elemen s om he queue and w i ing
ha in o ma ion o disk. In his way way, he impac o I/O ope a-
ions is lowe .
3.7.2 Simula ion Playback
A simula ion playback is he e e se p ocess men ioned abo e:
The da a is loaded om he ile, and applied o he simula ion ob-
jec s. The way o doing his is ai ly simple: I he simula ion is
s a ed in Playback mode, he p ope s ep() me hod o he agen is
no called. Ins ead, he in o ma ion e ie ed om he simula ion
ile is loaded in o he agen , changing i s p ope ies di ec ly.
57

3 . MASSIS O e iew
3.8 Visualiza ion
You mus see i s be o e you can belie e.
Raymond Holliwell
The isualiza ion o wha 's happening du ing he simula ion is
one o he mos impo an pa s o any simula ion amewo k. Al-
hough MASON p o ides good suppo o his, i is mo e o ien ed
o g id-based models, and adap ing he MASON display modules o
mee he equi emen s o MASSIS en ailed mo e wo k han c ea -
ing a new display sys em. MASSIS isualiza ion is composed in wo
pa s: 3D isualiza ion and 2D isualiza ion. The 3D isual in e -
ace is mo e sui able o ha ing a ealis ic iew o he building, and
wha is happening inside, bu he 2D display is mo e use ul o
es ing and debugging he model. Also, i is easie o he p og am-
me o ex end and manage.
3.8.1 3D Visualiza ion
The 3D iew is based on Swee Home 3D in eg a ed iew. I
shows he building and he elemen s inside i in he same way ha
Swee Home 3D does, bu some modi ica ions mus ha e been made
in o de o make i as e o displaying he model in eal- ime.
Tha is because Swee Home3D is in ended o being used as a
home-design p og am, no as a simula ion model display. Swee -
Home 3D i has been designed using a MVC pa e n, and has lo s
o e en lis ene s ha a e execu ed when an elemen o he building
is changed.
58
3 . MASSIS O e iew
Al hough ha 's a good way o s uc u ing an applica ion like
his, i 's no needed o MASSIS. In addi ion, i is no h ead sa e.
The e en s lis ene s we e emo ed, and also some in e media e lay-
e s, in o de o gain execu ion speed. MASSIS a chi ec u e accesses
s aigh o wa d o he 3D modules, a oiding in e media e calcula-
ions. Figu e 40 shows an example o MASSIS' 3D isualiza ion.
3.8.2 2D Visualiza ion
Laye -Based Display
MASSIS' 2D display is based on laye s. Each laye can be de-
signed independen ly, and i is in ended o displaying he simula-
ion in an schema ic iew.
59
Figu e 40: MASSIS' Real-Time 3D Visualiza ion
Figu e 41: MASSIS' Laye Selec o
3 . MASSIS O e iew
Laye s a e d awn one on op o each o he , ollowing an o de .
They can be o de ed, enabled and disabled. Lis ing 11 shows he
code needed o one o he mos basic laye s: Walls. Figu e 41
shows how he laye s can be enabled o disabled di ec ly om he
GUI, om an use - iendly checkbox lis .
MASSIS' buil -in laye s
MASSIS comes wi h se e al basic buil -in laye s, o showing
ooms, doo s, agen s, adios, pa hs, pa h inding u ili ies...e c. Fig-
u e 42 shows a combina ion be ween he obs acle laye , agen s
laye , agen 's adius laye , pa h inde laye ,walls laye , ooms laye
and doo s laye .
Howe e , new laye s can be designed wi hou modi ying he sim-
ula ion engine.
60
public class WallLaye ex ends Floo MapLaye {
public WallLaye (boolean enabled) {
supe (enabled);
}
@O e ide
p o ec ed oid d aw(Floo loo , G aphics2D g) {
g.se Colo (Colo .GREEN);
o (SimWall wall : loo .ge Walls())
g. ill(wall.ge Polygon());
}
@O e ide
public S ing ge Name() { e u n "Walls"; }
}
Lis ing 11: Example o one o he mos basic MASSIS' laye s: Wall Laye .
Figu e 42: Laye combina ion example
4 . Ge ing s a ed wi h MASSIS
4 Ge ing S a ed
Wi h MASSIS
The sec e o ge ing ahead is ge ing s a ed.
Ma k Twain
4.1 Ins alla ion
4.1.1 Downloading MASSIS
MASSIS can be downloaded om i s main eposi o y,
h ps://gi hub.com/ pax/MASSIS. The dependencies o he p ojec ha e
been managed wi h Ma en, making easie he dependency manage-
men . Fo ge ing MASSIS and all i s dependencies:
1. Download ma en (h ps://ma en.apache.o g/download.cgi)
2. Clone MASSIS eposi o y (o download i as a zip ile)
3. gene a e MASSIS execu able using Ma en:
m n clean ins all
4. This should gene a e a zip ile (MASSIS.zip) con aining he
execu able ja ile and all i s dependencies.
Some o he dependencies o he p ojec a en' in any public
ma en eposi o y (such as Swee Home3D). To sol e his issue, a e-
lease ag has been c ea ed, con aining hese equi ed lib a ies and
MASSIS' plugins ja ile. The con en s o libs.zip mus be placed
unde he olde pax. g. The u l o his ag is
h ps://gi hub.com/ pax/MASSIS/ eleases/ ag/ 1.0
The ins alla ion p ocess o MASSIS is minimal. In ac , i he en-
i onmen designe is no needed, i does no equi e ins alla ion a
all. Fo using MASSIS' Swee Home3D plugins, he MASSIS plugin
ile ( pax. g.sh3d.plugins.me ada a.MASSISMe ada aPlugin.ja ) mus be
61
4 . Ge ing s a ed wi h MASSIS
4.3 Speci ying Beha io s Wi h
Reac i e Plans
4.3.1 Ins alling Ne beans and Pogamu 's
yaPOSH edi o
Al hough POSH eac i e plans can be designed by hand, i is
much easie o do i g aphically. Pogamu 's plugins o Ne beans
help o his ask, in eg a ing in he IDE a g aphical plan edi o .
No e: This is only a sugges ion o a as e de elopmen o
POSH Reac i e plans, i is no necessa y o unning o ex ending
MASSIS.
Downloading & ins alling Ne beans
The Pogamu 's plugin in e es ing o MASSIS is he POSH plans
g aphical designe (yaPOSH edi o ). I is de eloped o he Ne -
beans pla o m (so y, eclipse lo e s!), so a ecen copy o Ne beans
(7.3+) should be ob ained. This can be done h ough Ne beans
download page [51]. Also, Ja a JDK 1.6+ is also needed (I can be
downloaded bundled wi h Ne beans, check he op ions in he down-
load page).
YaPOSH edi o
The Pogamu 's yaPOSH edi o comes wi h he Pogamu UT2004
ins alle . This ins alle can be downloaded om Pogamu download
page [52]. The la es s able e sion is ecommended (Fig 53). The
ins alla ion is ai ly simple, bu in case o any ouble he Poga-
mu 's download page has de ailed in o ma ion abou he ins alla-
ion s eps.
68

4 . Ge ing s a ed wi h MASSIS
4.3.2 POSH Plan c ea ion
Basic Beha io s Design
A good u o ial abou how o c ea e SPOSH plans wi h Pogamu
can be ound a :
h p://pogamu .cuni.cz/pogamu _ iles/la es /doc/ u o ials/ch14s03.h ml
4.3.3 Linking wi h Swee Home 3D & MASSIS
Using MASSIS me ada a edi o , he beha io o each agen can
be speci ied in he ields plan ile and classname. Figu e 48 can
se e as an example. The alue o he plan ile ield mus be ela-
i e, ne e absolu e (As will be explained la e , he loca ion o he
esou ces olde is a pa ame e o MASSIS launche ).
4.4 Simula ion
4.4.1 Running a new simula ion
Fo unning a simula ion, he pa ame e un-as mus ha e as
alue SIMULATOR.
In addi ion, as e e y simula ion (despi e he ype o i ) meeds a
building ile, his should be p o ided also. The pa ame e build-
69
Figu e 53: Pogamu Pla o m download page
4 . Ge ing s a ed wi h MASSIS
ing-pa h, ells MASSIS whe e is he building. The numbe o
s eps desi ed can be p o ided. I hey a e no , he simula ion uns
in an in ini e loop.
The pa ame e simula ion-mode should be SIMULATION.
Fo logging he simula ion esul s in o a ile, he pa ame e
sa e-simula ion- o (op ional) should con ain a alid pa h o
dumping he simula ion imeline.
ja a -ja MASSIS.ja -- un-as=SIMULATOR –building-pa h
<BUILDING_PATH> -- un- o <STEPS> --simula ion-mode SIMULA-
TION
--sa e-simula ion- o <SIMULATION_RESULTS_FILE>
The desi ed way o displaying he simula ion p og ess (g aphi-
cally o by console) should be speci ied wi h he display pa ame-
e (GUI o CONSOLE).
4.4.2 Loading a sa ed simula ion
Fo playing a p e ious sa ed simula ion, he alue o he pa ame-
e simula ion-mode mus be PLAYBACK and also, he pa ame e
load-simula ion- om should be p o ided.
ja a -ja MASSIS.ja -- un-as=SIMULATOR –building-pa h
<BUILDING_PATH> -- un- o <STEPS> --simula ion-mode
PLAYBACK
--load-simula ion- om <SIMULATION_RESULTS_FILE>
4.4.3 Help
The MASSIS launche con ains a help wi h a b ie explana ion o
hese pa ame e s men ioned be o e. Calling MASSIS wi h he a gu-
men -help shows all he pa ame e s a ailable and hei explana-
ion.
70
5 . Concluding ema ks
5 Concluding
Rema ks
I 's mo e un o a i e a conclusion han o jus i y
i .
Malcolm Fo bes
5.1 Conclusion
This wo k has p esen ed MASSIS, a mul iagen -based simula ion
amewo k ha suppo s he decision making p ocess o humans
when sol ing p oblems. This is achie ed by simula ing each agen
indi idually, bu wi h he suppo o se e al me hods and e icien
da a s uc u es ha ake ad an age o pa icula i ies o he indoo
domain.
The c ea ion o he en i onmen s is done by Swee Home 3D,
wi h some ex ensions o linking agen 's beha io in he simula ion.
Agen beha io is s uc u ed in low-le el and high-le el beha io
componen s, ex ending Pogamu ’s POSH implemen a ion model,
wi h he addi ion o ea u es ha acili a e he sepa a ion o deci-
sion making p ocess and low le el ac ions.
MASSIS p o ides a ich se o low-le el beha io componen s o
he simula ion o indoo scena ios. This has equi ed o MASSIS
he ex ension o he Swee Home3D en i onmen wi h plugins o
linking agen ’s beha io in he simula ion. In o de o apply MAS-
SIS o o he kind o scena ios (e.g., a ci y), new low-le el beha io
componen s should be implemen ed and in eg a ed wi h ano he
g aphical design package ha suppo s he de ini ion o he new en-
i onmen .
In his sense, MASSIS can be easily ex ended. MASSIS p o ides
as well a ich log capabili y, which can be he basis o u he anal-
71
5 . Concluding ema ks
ysis o he scena ios. The ex ensibili y o he MASSIS pla o m is
well suppo ed h ough i s componen based a chi ec u e. Fo in-
s ance, di e en isualiza ions can be managed du ing he simula-
ion, new algo i hms and agen a ibu es can be suppo ed and
moni o ed.
5.2 Fu u e Wo k
MASSIS amewo k is only in i s beginning, and we belie e has
huge po en ial. In he nex e sion o he amewo k, he e will be
many upda es o he amewo k. The mos ele an a e shown be-
low.
In eg a ion o exis ing analysis ools
One o he mos ele an issues in he nex e sion o MASSIS
will be he in eg a ion o exis ing analysis ools, in o de o make
his amewo k mo e independen and use ul.
Di e en AI beha io models
I is in e es ing o add new, di e en easoning models, in o de
o es di e en app oaches.
Using a mo e powe ul 3D engine
Al hough Swee Home3D is good o his ini ial e sion o MAS-
SIS, he e a e be e al e na i es (such as Jmonkey Engine [53]),
wi h which mo e ealis ic esul s can be ob ained.
Dis ibu ed Compu ing
Ano he ea u e ha is being conside ed is making MASSIS ca-
pable o un o e clus e and cloud-compu ing a chi ec u es. Since
MASSIS uns o e MASON, and he e is a e sion o MASON de-
signed o un in pa allel (D-MASON [54]), i would be ela i ely
easy o implemen i , and i would add mo e alue o MASSIS.
72
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7 Appendices
Appendix I: Ra ael Pax, Juan Pa ón: Agen -based Simula-
ion o C owds in Indoo Scena ios. 9 h In e na ional
Symposium on In elligen Dis ibu ed Compu ing
(IDC'2015), Guima aes (Po ugal), 7-9 oc . 2015 (accep ed
o o al p esen a ion and ull publica ion)
E-mail o he no i ica ion accep ance:
Subjec : IDC'2015 no i ica ion o pape 20
F om: "IDC'2015" <idc2015@easychai .o g>
Da e: 05/22/2015 05:49 PM
To: Ra ael Pax < [email protected]>
Dea Ra ael Pax,
We a e glad o in o m ha you submission
Agen -based Simula ion o C owds in Indoo Scena ios
was accep ed o O al P esen a ion a he 9 h In e na ional Symposium on
In elligen Dis ibu ed Compu ing (IDC'2015) and o publica ion as a Full
Regula Pape (max 10 pages) in he IDC'2015 Con e ence P oceedings. You can
consul he de ailed e iews a he end o his message.
The Con e ence P oceedings will be edi ed by Sp inge in a olume o he
Se ies S udies in Compu a ional In elligence.
In a ew days we will send you a sepa a e message wi h ins uc ions on
how o p epa e and submi he came a- eady e sion o you pape . The
deadline o submi ing is June 19 h, 2015.
All accep ed pape s mus be p esen ed o ally a he Con e ence.
A leas one au ho o each accep ed pape mus egis e o he
Con e ence. The s ic deadline o au ho egis a ion is June 19 h, 2015.
On behal o he IDC'2015 o ganiza ion eam, hank you o submi ing
you wo k. We look o wa d o seeing you in Guima ães nex Oc obe .
Bes ega ds,
Da id Camacho
Paulo No ais
I