toma de decisiones artificial

8/22/2019 Toma de decisiones artificial

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ifil eiion king nde neiny in neigenuilding

Mas Ba Pau Davss Hk L. YuDSV Stkhlm University and

the Ryal Insttute f TehngyEletrum 23 SE64 4 Kista

Sweden [email protected]

Dept f Sftware Engineering andCmputer Siene

Universty f Karlskrna/RnnebySft Center SE372 2 Rnneby

DSV Stkhlm University andthe Ryal Institute f TehnlgyEletrum 23 SE164 4 Kista

Sweden lrens@amrgSweden pdv@idehkrse

Absrac

Our hypthesis is that by equpping ertainagents in a multiagent system ntrlling an

intelligent building wth autmated desinsupprt tw imprtant fatrs will be in-reased. he rst is energy savng in thebuildng The send is ustmer valuehwthe peple in the bulding experene the ef-fets f the atins f the agents. We give evi-dene fr the truth f this hypthesis thrughexpermental ndings related t tls fr arti-al desin making A number assump-tns related t agent ntrl thrugh mnitrng and delegatin f tasks t ther kndsf agents f rms at a test ste ae relaxedEah assumptin ntrls at least ne un-

ertainty that mpliates nsiderably thepredures fr seleting atins part f eahsuh agent We shw that n realisti de-sin situatins rmntrlling agents anmake bunded ratinal deisins even underdynam reatime nstraints This resultan be and h been generaized t therdmains with even harsher tme nstraints.

BACKGROUND

We have taken a multiagent systems apprah t intelligent buildng ntrl. Our test site Villa Wega inRnneby Sweden s a threestry researh labratryequipped with n Wrs and deves fr mmun-ating n the eletr grd Mving frm simulatinand visualizatn f events and f the physia appearane Villa Wega t ful eded mplementatin hardware ntr we must slve a number fdiult prbems sme f whih we have addressedalready Bman al. 1998 We reprt here n an

1Se wv.echlon cm

attempt at imprvng results previusl btained bletting agnts use autmated deisin supprt whenfaed with situatins in whih unertanty plays a vitalrle We will desribe ur apprah using the intelli-gent building dmain thrughut the paper but in theend the usefulness f ur apprah shuld be bviusals r ther dmains.

he bjetive f the agents is twfld: energy sav-ing but als inreased ustmer satisfatin thrughvalueadded servies As wll be shwn belw the abil-ity t reasn under unertainty is relevant t bth b-

jetives Energy saving is realized eg. by lights being autmatially swthed and rm temperaturebeing lwered in empty rms nreed ustmersatisfatin s realized e.g by adapting temperatureand ight intensity arding t eah persns persnalpreerenes Our simulatns indiate that signiant

savings thus far up t 4 per ent an be ahieved(Davidssn Bman 998. We nw laim that fur-ther savings wuld be pssible if agents were t hseautnmusy and ratinally between atin alterna-tves in reatime situatns rather than resrting thardded atin patterns. n ur implementatinthe use f pan libraries is therefre restrited t statiplans he latter are essentay sequenes f primitiveperatins whih rarey require rearranging

he mutagent apprah alws fr a struture-preserving mapping f the design entities f the app-atin and f the smart euipment f the implementa

tn t s an pen arhiteture in whh agents an beeasly ngured and rengured even dynamiallyin the sense Cheyer Martin Mran 1999) It isals truly distributed sine we make n sumptinsabut the latins f the agents

Setn 2 very briey desribes sme f the agents inVa Wega Sen 3 mvaes he need fr agen de-isin supprt and why agents in intelligent buildingsmust reasn under unertanty Setin 4 summarizesur reent ndings in artiial deisin making andthe penultimate setin gives an example f hw they


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Boman, Davidsson and Younes

can be used in Villa Wega We clse with cnclusinsand relativel extensive indicatns f nging and fu-ture research

AN INTELLIGENT BUILDING

In Villa Wega, each electrical devce is cnnected viaspecial purpse hardware ndes t the nWrks ssem, allwing he exchange f infmain ve theelectrical netwrk Sme f these devices are sensrand sme ae actuatr devices The sensr devicemst relevant t this paper is temperature, and tsme extent an active badge sstem. The latter makesit pssible t knw which persns are n each rm atan mment Harter & Hpper 1994) The actuatrdevices in the current applicatin are lamps radatrs,and generic mbile devices that can be cnnected tan arbitrar electrical device, eg a cee machine, ra persnal cmputer It is pssible t switch n and

the device cnnected t he generic mbile deiceand t read its state

These devices interact with and are cntrlled bthe multiagent ssem MAS, implemened in ApilMcCabe Clark 1995) The sensr devices pr-vide input t the sstem and the actuatr devices c-cinall receive instuctins frm it This interac-tin is mediated b a cntrl panel written in Javathat translates messages frm the MAS t cmmandsunderstd b the nWrks sstem, and vi vrsaCuenl, he entire building envinment can be sim-ulated, including the cntrl panel functinalit (seeFigure 1) In additin a GUI visualizing the state fthe building in terms f temperature, light intensit fthe ms, and the persns present in the rms hbeen implemented (see Figue 2) .

Multi-agent

syem

(MAS)

MAS Cono pane

nerface

Envronmen

uazaon

GU!

Smuato

edor and

execuor GUJ

Figure 1 Simulating Villa Wega

There are several categres f agents in the MAS (seeeg, Bman t al 1998) f details) We need t cn

2For informato on the so-called ARO Swtch Staon, see igo d/indx- ht

sider Room agntswhich each crrespnds t and cn-trls a particular rm, with the verall gal f savingenerg Taking int accunt the peferences f the per-sns currentl (r sn) in the m, it decides whatvalues f the envirnmental parameters, eg temper-aue and ligh, ae apppiae Envionmntal Paramtr EP agnts then have access t sensr and

actuatr devices fr reading and changing parametersFr instance a temperature agent can ead the temperature sensr and cntl the radiatrs in the m

DEISION SITUATIONS ANDUNERTAINTY

Usuall, the gal f a Rm agent and agents realiz-ing user preferences in the rm are cnictng: TheRm agent ties t maximize energ savings whilether agents tr t maximize custmer value n the

inelligen building dmain, his is the mn tadeAnthe tpe f a cnicting gal situatn can be exemplied b the adjustment f temperature in a meet-ing m in which peple with dierent preferencesregarding temperature will meet The preferences feach persn in Villa Wega are encded in a PrsonalComfot agnt n gd time befre a meeting stats,ersnal Cmfrt agents representing each persn paticipating in the meeting negtiate abut the tempera-ture When a particular temperatue has been agreedupn, the Rm agent RmMeet delegates a tk tan E agent cntrlling, eg, a adia Secin 5gives an example where E agents mst chse be-

tween vaus actins aecting radats and ventila-tin in der t achieve their gals This is a tpicaldecisin situatin that shuld be slved b analsisand evaluatin f altenatives This prcedure in turncalls fr articial decisin making capabilities in theRm agen

The Villa Wega meeting rm s equipped with tw1000W radiatrs When the meeting rm is empt,he tempeaure is set t 6C There are n averageve meetings per week, and the length f each meetingis tw hurs n average The persns in Villa Wegahave electrnic calendars indicating, eg, which meet-ings the will participate in This infrmatin is avail-able t RmMeet which ma use it t plan fr theheating f the meeting rm in a wa that minimizesenerg cnsumptin Hee, uncertaint enters the pic-ture A persn might nt shw up at a meeting Theextent t which a persn acts in cncrdance with herelectrnic calenda naturall varies and the prbabil-i her shwing up at a paicula meeing culdbe taken int accunt Machine learning algrithmsntwithstanding, RmMeet can be infmed f pre-cisel wh is in the meeting rm at a timepint, sa,


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Afcal Decsn Makng

"

I rune:. 10 00 ' 0 i 00 0 ' 00 0 : 700

0

martin

:t 210

300 0 100

0 100

Figure 2 GI Visualizing the State f the Building

ve minutes after the planned start time This ifr-matin an be btained frm the smart badge systemRmMeet an then all fr a renegtiatin f thetemperature setting with the ersna Cmfrt agentswhse wners are atuay at the meeting After a fewsends a new setting is btained Mst likely thepersns at the meeting will nt even ntie but pssibly appreiate the slight hange f temperature thatmight ur sn after RmMeet des nt demn-rae here muh reasnng unde unetainty a andaptin t a situatin deviating frm the respnd

ing expeted situatin due t an unertainty

In rder t mpute the time required t adjust thetemperature in a rm the Rm agent relies nthe thermdynamial mdls desribed by nrperaand Witt 199) disretized arding t standardpredures desribed by Ogata 199). The therm-dynamia haraterstis f a rm are desrbed bytw nstants the therma resistane whih apturesthe heat lsses t the envirnment and the thermaapaitane whih aptures the inertia when heating

up/ ling dwn the entities in the rm see Davids-sn & Bman 1998) fr detais We have als madea number f simpliatins that aet RmMeet inur example suh as

Outdr temperature is ignred

Radiatin frm the sun is sumed t be negligible

Radiatrs have an eieny f 1 per ent

Heat prdued by persns n a rm s gnred Heat prdued by mputers lamps and ures

ent tubes is ignred

Relaxing any f the abve sumptins means thatpredures beme mre aurate Hwever all butthe rst impiatin have a very sma eet n anydeliberatin in RmMeet Fr example the eets f

3Th vrg 10 C ws usd n or mrmns ongy vngs.


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8 Boma Davidsso ad Yous

sun radiaion are o a lage exen capured by oudoorhermomeers paced on a four sides of he bu-ing Noe also ha some of he facors would increehe room emperaure whie ohers woud decree i.Oudoor emperaure does have a very srong eec he dierence beween a ho summer day and a coldwiner day can be more han 5C in Ronneby. heprobabiiies considered in our exampe will hereforebe condiioned on he unknown ouside empeauremaking he example more dicul bu also more in-eresing.

4 AIFICIAL DECISIONMAKING

Numerous oos for decision anaysis are readiy avail-abe o human decision makers aiding hem in hesrucuring and solving of decision siuaions by means

of inuiive GUs For an aricial agen oher meanso inerac wih a ool han hrough a GUI are neededWe have invesigaed a number of commercial andacademic oos for decision analysis and have concluded ha mos of he available ools do provide inerfaces suiable for agen ineracion (Younes 1998)We have run ess on hree oolsNeica SMIE andDAA Ineracivein he RoboCup domain. Neicaand SMIE are based on he algorihm of Shacher andeo 992) for nding he opima policy in an inu-ence diagram. DAA Ineracive uses decision rees orepresen decision problems and is based on he averagig out ad oldig bak algorihm (Raia1968)

We choose o pu he decision analysis funcionaliy ofa oo ino a proouer (Boman & Verhagen 998)his is an auhoriaive eniy exerna o he agens inhe MAS. he agens call upon he pronouncer whenhey are faced wih a decision siuaion afer whichhe pronouncer evaluaes he given problem and re-urns an acion o he agen. Normally his would behe acion ha maximizes he expeced uiliy for heagen bu he pronouncer could make use of normso ler advised acions in order o accoun for groupuiliy in a MAS (Boman 999) or of consrains represening risk aversion (Ekenberg et al 1999)

he alernaive o using a pronouncer woud be o havea separae deiio module implemening he decisionsuppor funcionaiy in each of he agens. Whilehis would reduce he response ime for each decision query i would also make each agen subsaniallylarger (ounes 998) he aer can be a problem inour inelligen building appicaion if he agens are obe disribued hroughou he building and no onyreside on a cenral server. n he elded applicaionagens may even ranspor hemseves on he elecricgrid and hence keeping heir sie moderae is of in

D: how to acheve temp

Figure 3 he Decision roblem Represened anInuence Diagram

2 radators on vent. off

I radator on + vent off

D: how to acheve temp

no radator on ve o

no radator on + vent on

Figure 4: Firs evel of he Corresponding Decisionree

eres. Moreover he onWorksrelaed hardwae insaled in Vila Wega also h limiaions wih respeco memory he ime consrains in our domain arealso no severe enough o moivae he use of decisionmodules.

5 EXAMPLE

he decision problem oulined a he end of Secion 3can be modeled wih an inuence diagram (Figure 3).he decision node represens four possible acions hahe Room agen can perform in order o adjus heroom emperaure (see Figure ). he decision willinuence he nal oucome of he room emperaurebu will also inuence he uiliy funcion since eachacion has a dieren eec on he energy consump-ion. he fuure ouside emperaure which consi-ues he uncerainy in his siuaion is modeled asa random variable wih ve possible oucomes rangingfrom high posiive dierence beween he ouside em-

peraure and he desired room emperaure o a highnegaive dierence. Finally he chance node repre-sening he na oucome of he room emperaure hashree possible oucomes: he emperaure is higherhan desired he emperaure is desirable or he emperaure is lower han desired.

We have run 10000 se/evauaeruns on he given in-uence diagram using Neica and SME in order odeermine if i is feasibe o use eiher of hem for de-cision suppor in he Villa Wega agen sysem. Wehave also ransformed he inuence diagram ino a de


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cson r whch w hn hav valuad usng a bascdcson r valuaor (BDTE) plnd b our-slvs basd on h avragng ou and foldng backalgorh. Th prforanc asur usd n all sswas h aks o rs s all valus n h odland hn valua h who odl Th pafor was

a 167 MH Spar lra Craor runnng SolarsIn our plnaons w do no allow h agns oforula dcson suaons on h snc wouldb xrl dcul for h o do so Boan 1999)Insad h pronouncrs conan pa odls dsgnd n advanc for dcson suaons ha can bprsud o occur Each pla drns hsrucur of a cran prob whl s up o hagns o spcf h valus How hs valus ar scan var wh h plnaon n addon o husua wa of addng valu nods o an nunc d-agra varous odls of prdcon can b adopds .g. Ygg & Akkrans 1997).

As Tabl 1 shows vn h sows oolSMILEsolvs h dcson probl a hand n lss han nllsconds. Wh hs shor rspons h agnscould ak xnsv us of h dcson suppor pro-vdd b a pronouncr whou an nocabl dgradaon of h ss Ths s an poran pon asan rsarchrs hav akn a sanc agans raldcson suppor for arca agns. W hav hr-for ad an or o no on donsra ha hus of pronouncrs s fb n an doans bualso o pln h

Tabl 1: Mans and Sandard Dvaons (n llsc-onds) for 10000 Runs

TOOL

BDTENcaSMILE

MEAN

003.17.31

ST.EV.

0010.1718

6 CONCLUSIONS AND FUTUREWORK

B nvsgang a nonrval proopca xaplw hav donsrad how agns par of a ulagn ss conrolng pars of an nllgn buldng can raon undr uncran h agns akcalls o a pronouncr whch provds xr fas d-cson suppor b valuang h npu a dcson ror an nunc dagra) and rurnng h bs ac-on. W hav no ld h scop of hs suppor bassung a parcular dcson rul .g. h prncplof axzng h xpcd ul. On h conrar

Aifiial Diion Making 9

w ar nrsd n how drn clascal nsonsofh prncpl such rsk auds Eknbrg e al.1999) group raonal Boan 1999) and arulsLask & Lhnr 199) ac agn bhavor.

Wha s an b h bs acon s drnd n parb h naur of h socal spac sos calld

an arcal coss) ha h agn s n. For n-sanc on agn can b par of svral coalons achof whch consrans s acons consdrab whl an-ohr agn s or ndvdualsc W ar currnlnvsgang h us of nors o achv socall n-llgn bhavor n a nubr of doans ncludngnllgn buldngs Analogous o our olran vwon dcson ruls our vw on chncal nors s havarous pnaons (.g. acv/passv nors)ar worh of sud and ha anors (Axlrod1986) us co no pla.

h hgh spd of h plnd pronouncrs ak

svra xnsons of hr funcona possbl. Thcorcal ools nvsgad hav aracv faursha canno b usd n doans wh svr con-srans .g. RoboCup bu whch a prov os usful n nllgn buldngs On such xnson s ovagu and prcs daa. Th prcs valus handldb h pronouncrs dscrbd n hs papr ar awk-ward and unralsc n an suaons. W ar hr-for nvsgang h possbl plon of our algo-rhs for valuaon of suaons wh prcs val-us orgnall dvlopd for anagn sss andhuan dcson suppor (s .g. Eknbrg Danl-son & Boan 1996) (Eknbrg Danlson & Boan

1997)) n arca dcson akng

us alwas b possbl o ovrrul h dcsonsof h agns n h MAS b phsca nracon whh crcal qupn For nsanc vn f an EPagn has dcdd ha h gh n a roo should bon us b possb for a prson o urn o h ghusng h swch n h acua roo. hs consransar of cours no hardwrd no h MAS and canb changd asl. W hav sudd h probl ofanua ovrrds and s cs on agn auono(Vrhagn & Boan 1999) and nnd o pursu hsrsarch n h doan of ngn buldngs.

As h abov sng of ongong and fuur rlad r-sarch ndcas nllgn buldngs s no h ondoan of nrs o h proposd hods algorhs and pnaons W hop o hav adclar ha b choosng a proopcal apl ournnon was onl o ak a prsnaon absracnough o alow for sraghforward appngs o ohrdoans.


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