Multiple sensor

8/12/2019 Multiple sensor

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no seveal distnct dcsionme These modules e organzd nd coucae n such a mnr as ceve he coon go of e sysem gazaon ofis e of ae ofn fed [81] We rose o conside a eam heotic oulaion formulaionof multenor ysm in he foowg snse e n consider as mebers of the , eh obsrving heenvirnent and mking loca dsions b on e informaon avilable e. A maner (euve or coordinatr) mkes se of utili considrations onverge eoinions of the sensor syse Secon 2 wi be devoed a review of team decision theo an present some newanalyic resuls[7]

One ctci of deision o is at opl solu-ons re on dfcult o possibe to fnd In o oai in analysis of thes obems e have ult a sla-on enviroen We use he simlaon exne vousnonoptil nd heurisc soluions oeise inaclepbe, epeent w ffent oss funcons de-ene the chracr of the rsultnt decision meod. The

iulation is a generaon of classic puuit and evasion game s [4 ea of prsue an evae. Eh ember h loc senso d st vables They oodnad ough a exuive. Son w eevo d look at the siulaon d our sults da

e feel a e ea folaon of ns sysh iplications for e bro study of cial Inll-gence AI is levant t is wor in a least sp:

Fsy, it is cy possie considr e gentsof e sys s perong oning cessConsideng AI syste as disionme seems alausile apph o e conscon of nlligentdsbued systms. Thus ths wo h commonal-

es wi Diud AI o teresd inquestions of scng ifoaon d conica-ion bewn nelgnt sys.

Sondly we ofn wt t e foaonavilae sysm, an couicate nformaion reons rar siple sigsThs is pmily a pble i represetation of infor-maion. Again, AI has focus on e inptionof infoaton an e repsenon of a inrreaion

More generaly, we would discover when systems eis c be aly os dson proble. con wl e devo an in dp disussion of e genera is and shoomngs of e oional view d aempt

dene whe is most appropria u.

A Tem-Theeic Fli f

MlSe Sye

Te orgiaed om proble in game ry [26and ulipern conl e bis or e alyss of coop

eration amongst sctures wih dfent onions or nes was fomulad y N 20 he we own ganng roe Nash's ouon for e o eon cooeaegame w develod nto e concep of nfoaion, gouaoni and ulpeon disions y Savage 2 eam hs snce n etensiey used y cono

yze sc [6 nfoon [18 counicaonSon 2. induces e a sucre and denes encon of e tea ebs d mangr. Dierent amorgons dcussed d conceps of iformaonscte, a deision, tea ulity nd cooperation edened i Section 22. Seon 2.3 apples ese chniques he mutisensor am an a mehod for aggregating onons is deved. Due to lac of sace we wl sume somefamiy wi proali d dision o

Ta Plmas

nr or ebe of a of enso is cacd yis inormation scre an is dision funcon. onsid

a em compsing of " membs or sensors each mangosevtions of e stte of e envnment The nfoaon scte of e ea member is a funcion whichdees e carc of e sr obseations E nrms of the ste of e environment E nd he othernsor actions E j So that


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A senr or memb of a tam will be considered ratioal it c ple apreference ordeng on it acons atadmi a ulity unctio u; E One possible set of raonli iom c be fond in [2, p. 43] ad e pof thatese io adt a utii fction c be fond n [5 Adision le 6() can be evaluat in of i paff:

u;(6, 8)= u;(6(z;),8)f(z;l8)dz= E[u;(6(z;),8)]JWe sme at a rational team member is aempting omaximize i payoff

e team utili is a con which assigns a value ech team actio L( a1, a2, , an) The role of L is veryimportnt in chacterizig the team The interpretation ofam ton due Ho, Chu, Mscha and Rdnor [13,16],is that e goal of evy am member is maximize Lregdless of pesona loss (in actpeonal loss is not evendeed) We will cll this an "aluistic team An lternative fouation is allow individual am members tohave a personal utli as we an terest in e .Fr example a team member may agree cooperate and besbject the uiliy L or o disaee wi e other teammembe and be subject a onal uliy. In is ce aratoal te member agree o coopate y if it gin by doing so when the team utlity exceeds i persoalutli We shall call his tagonsc team

e idea o idividual rational c be extended toiclude socaled grop ratioali Nash rst inoduced aset of grop ratioa io The has ben considerable disaeemet abot ese io [28], d a nmber ofoher deitons have been suggested [10] The underlying bis for providng grop rationai is e abili of aam to pt a preference orderng on grop decisions. Unlike individa uti considerations, is volves a mb

o sumptions about e natue of e grop or team. reample, each am member mst assume me sbjectivekowledge of other player ratioali, inteersonal compisons of utii require preferences t be congent andassptos mst be made abot idiffeece, domacead dictship

2.2 Team Organizatons

Te pobles asociaed wi e etesio of idividal goup rationaty are al conceed with e comprisoo idividual utities The existence of a grop prefereceodering is equivalet o requiing that the combinatio odividal team member utlties that o e team utii,s conve. f ts is satised then we say at the gop

decision is also person-byperson optmal. The key pinciple in grop decision ming is e idea of Pet optmaldcision les

Di: The oup decisio 6 is Ptooptmal eveyote rule 6 E D decreases a oe team membes t

f the sk set of e team L(8 61, , 6n) Rn is cove,ten it ca be shown [13] that such a team decisio is also

personbypeson optimal so at for all team members = , e a ton = [a1, , an]T also satises

maE[L(o(z1),,a;=6;(z;),,6(zn))] 2EAf e cls of grop decison les D incldes all joily

randomized les then L will be conex. f we really believed in an aluistic team we mst use this clsd be subject to these res. Considerable work h beedone on ndig solutions to equato 2.3 under ese codions [16,13,12,11, prticully as regas the effect oifoation sce on disbted conol problem

We e primly iterested in ea o observe seso aing obseatos of the state of e evromet is case te team member c be considered as Bayesiaestimators, and e am decision is t come to a consessview of e obed state o nature. The satic team of estimaors is often called a ltiBayesan sysem [28] Thesesyste have many of e same chacteristics more geeral team decision proble eerhdi 7 as so

at e set of nonrandomizd decision les is not completei these syst If o te members usig decision les6 = [61,6:] have uites (8) = u1(61,) ad u2(62,8)en he team utli nction L(8) = L((8)) wl olyadt a consensus it saties e nequai

E[L((8)) L(E[(8)) This is e Jesen ineuai, and it is wel ow tat iswl b satised ad oy the fco L( ade sk set e convex. Generally, is will only be rewhe the set D of decisio les icldes jointly randomizddecision rules.

Cosider e team ut L a fncton o the teammember utites so at L L(u1, = L() Tegroup ratioaty principles described above resict e tions L that e of ierest ose at have the followig

properties[ ]

1 Uamty > 0

2 No dictr: f u; = 0, there is no U sch tatL= j

3 Indierece If 351,62 such at u;(61,) u;(62,ten L(61) L(62)

f the am utili fction L sases tese popeties ewill say at te team is ratoal The uction L is ocaled an "opiio pool To coo eamples of opiio

pools are the geeralzed Nsh prodctnL(861,. ,6n)=ciu(6;,8) ;

d e logic or e opiio pool

nL(8 On) > e


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The value of e gener Nh product c be seen bynotng at if u;6;)8)= z; ! an = en L e posor dens of 8 wh respect e obseaosz; A ccsm leveled a the galiz Nh pduct shat t sume ndece of opinos however s aybe cound fo ugh e weigh A icsm ofe ne opno poo is a e s no efoemen ofopion.

Suppose we now resc up ecision ues 6 E D non-ndoze disions. s lows tm mbe dsage i e foowing sese f e m s set u68 u68 covex fo ndod6 hen equaon 3 hols a cnn my be rch fhowver s concave at let on " if rdoes e dsalow s betr (n of ul) for eaaam mbe sa ey we gs agonis sud be cle fm example at e ife be oisc aluisca e abili ob a cnvex "opnin

f al the U ex ncons en Lwl l ways beconvex on cls of no-rdo decisons However loaon esimion MulBaysian sys e u willof be concae a w be gud convexon e class of rom les. hus L wl alwaysbe cove for a alsc tea Fr an antgonc tam Lwll only be convex when agrment c be reached n ecass of nonranomie dions), oeise if opnonsivege sucienty ten L wl be concave. Concav wgealy e e fo of seping mbs iconvex oups of opinons coaions whch my ovrlap

Ou n hese resu cns on ing whenamnt ca be reached d n calculatng e value of econnsus We sue ese concepts n e followng:

Rut onsder a tm w meber ues u6;9)an uly sasyng the gup aon condosThe

11 Cu oopeao wll oly occr whe e tof rskp L 6n) E Rn s x

1. Atruiti If 6 E [ is e cls of al radoddecson rles then L wll always be conve

1 Aagiti I V L u; then L wl be covex ne cls of nonrandod son les

14. Dagr When L cocave te is no bestdecsion ad agreement cannot be reached

The pot at whch L oes cocave for each ebers caled the dsageent poin e value of a eb'suly at s pot s cale e ecuty level

23 Muli-Sso Tams

The uso of sesor obseaons reques a we have amed fr mpig oato o dspaa sues.We cosder each senso o be a member of an anagosc

a n the olowng sense: Eh sensor coes up w u-ce partial views !f e stat o he envonn e goalof e ecuve s ngra e vous ser opnos,by oeng incentves d inrtaons for conng dspa ewot e agonstc sucu alowsembe dsage f for some reon ey have made a

se or cno concie ther vews with ose o teoer ms alisc am could no e thson

We sugges at e compson of divere obseaonsca be preted in ts of a copson of te uly oa connsus deson. Suppose we have o obsvas zd z2 which e o iry compable ach observtocnbus oe hgher level ecpt of the evom, and each epdent on e other We can nrety son6 ou e vronmet of i ut oe obsvaios u6z8 n u26z28 Althoughz1 d z2 cnot co drty, er conbuos pcu decisons can be evaua n a coon utlwork he am ec comprn of utes -

s a su of dsamen alows for e evalutionof sr ifoaon in a consis mneDene be e t of ss of are ad coder

a obo sysem wh nsos 1 J 1 m g seeces Of ObeatO = . { I n f featre ie evronme. We wl sct itert e satc asct so at Z = 6 caly, sensors can maedecisions ba on local obsvaons as 8 = 6;z mmpale equence z th espect ta coon uli u6;Z;) 9). Jonty the senor tea ha u L = L 6 6n), whch can be consdered a funcon of e idvidual ules L= Lu u2)sasfyng the group raona condtons

If he obaos from diffnt sesors icoparabe hey mus be nrpred me coon aewor

hs wil be the ce when e so e locatd n deret locaons for exale. Let D s obseaos e coon descpton aewor en the am lossca be wien as

uct[D)] uo[D)8)])By seecng L and alyng ts cove, we wll esalshe character o e sesor tea

The ty aos dev utii y requre hat we be able t put a pence ordeg o ecsions c It sees reasoable at he peeece damtt by obsato wll be he sae orderg asat o by a a lelhoo ear ayes raoat I hs ce e utty i bst wl b iidt wi s kihd uco

Thus e Gussi disibui Ni 1 A assid wi hobsevaton ; can so b sidd s th preerece ordeg o posror utiity fn o z o y suiestate 8 In ths raewor bsvtis d 1w have a bs or met ly i th mbid utlids hi idivid uiity, th is a ossus obe rd e set f bsevn uies m a cvexset

2


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To d z8 = fztl8 " Nz;, as loss e obatio Z o ema d le

deo e u e, o xped u, aconssus c oly be achd L sts Euao 3 e co L i cov

e fuco L be cov d oy f maxof sond ord dvavs s ogav de I Lsases e up aoa pcps s rues a 0 fori= Dffenang ves

a83' = ( 6- z;)' A/1 6 z;) Nzt,)r es o b pos, ad hec e R n beconve, we e ust d a whch sass:

0- z)/ 8 z;) S (4)Fr l = 1, obseaiosCosder y wo obsvaos z; d ZJ They c

fom a consesus c nd 9 that sas uaon4 o bo z; d ZJ To comp obseao e etem a coon f rmwork D(z; ad D1(z1 J and Ji e jobs of D d D spvy [6de .E;= Ji AiJ Ths i e foon maxof obsatio Zt ansfo o e oo frm of

nc by h asfoaon D.Sinc lt and de o uao 4 is aas poste

ust a 8 hc sass

8 D;z?E 9 D;z;) +(0- DjZ)i)TEj(0- DjZi)) s {)

The value of 8 wch ms h f hand sid of hs qua a nmum d whch i also the consnus hnt exss) s ge by h s combaon o oaobsevaios[]

8 E;+ri l !;D;z) + DziSubsiing hs no quation gvs

iD;z;)- Dzi))'+Ei 1Ei Dz)Dizi)) (6

We wil say at z; d Zi adt a Bayesan (nonradomizd cosnsus d ol h satis uaio6 Th left sde of uato 6 whch e will den asd

s clled e geezd Mhalaobs dsc (a -

sctd for o ths s ded in 27 d s a measure odiageen beween observao Fr 3 showspo o agast Uj fo vous vas o 1 ad whiccley demosat at e convexiy of h set [u; Uj]corsps qug at d S Ths measu c befuher exdd codr more ha o obsevaos a aime. Fr ample, if each obsation z; has

v

v,

Fgue : Po o ahalaobis distancs

sam vaccovace ax hen a cossus bobd oly

i:1 n d


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char e classic difenal gae o e i uf 1} That classca fo of i gacosis of a puuer d evader movig at constt veloci a plae. Both playe have pect nfoaon about eoer's sta, and aempt use s inormation incept

or eve he oppnent pevely. e payoff scof he gae i e e un cape The ajor chges weha made at we have upp e plays wh impeft ssng devices (e e plays use ect sinfoaton) d we alow pe psus d evsgupd nt eas dad by a am execuive It isimpornt no a e ovon for sing e psuitevo amework s pmy rovde eh wa wedened mehod for compng sucs and conolpoicies. The game s not of innc alue by ief butfos a sc exble, closed sysm in which sensormodel, organizona scs and dcision meods maybe mpemend d ey evaluad

The slan consc so at w c vy sce of mee as we s ovall scre, quckly evua the effec of e chge bad on echr of e siulat ga at enses We have md alo vaon i suh f dac, sr, fon iatin oces, centive scturesa ucrai of nfoaon and obsee wha pes ofpolices lead he eua poance n e crcum-snces We expect spot what we le m hesuaon reald pbe musenso bot sys cuently beng develope n the Gras abo[21}We magne a son i sl provde aenvroent in which isbu expe orinaon andco pbems c be nvesgd efo impenaioad conveely a applicaos of he senso sys unddevelopme wl sugge what dcons nsor modes anddyams wld mt il eplo n e

suaton.The emader of s con deas cn scre ofe silaio envnmen d outlies our ial expe-eces ih i

31 The Game Environment

The imlan place o a plan eld bl lredith obsacles. The c cyce execun ove ammembes g er readgs, exuives ingrag -fman d offg ncentves and nay mmb mkig decsons The sta vables dad e gae moves a e sep A game temas henad f h r rb;which e uippd w a imlballic m car all vad This i a mediumlvl f gal emhasi on the gnal bhav fa n h c f memberSm m-cna ues can be nvestgat by ncudng me pamers in e payoff fucos, but comunal compleiy d invgaion of ychoousbhav are oide e c f coderao Frce f ome decin lic s cmuaina mrcmpex han anhr ffrnc n fmanc wl o

a omplexi

32 The Sucture of Tea Mebers

he chac of ndidua am membr s ded

e modues: The inematcs d dynamics of moton o e plae,

2 what seso avlable d the nose chacr-stics of ose sensors d ther kinematics ad d

namcs, ad

3 he bastcs which deie e rnan f thegame.

he me const veloc, vable d-cton ui opating a ple w a variabl z, d 8 Since robo move wi constt veloc, eonly dcy cold vaable is 8 The only dyaiaconsideraon vlvd hw we low robot hge

s cunt heag some ew deired headng 84 hsngle col C ny we sume that when roieigeach agent can move wih some ed (obly ie eloci . he ha ffc f deig a mim gd. Prr gneally av m n w whl evadehave y m nsos

The senso model w cnl ng a range dicon sesor The sensor has a d coe of da gaing, ad a rge It h a sngle cool vable which robot c selt ont e nsor We sume at n cally nosy daa, we havedin noie moe whch "ap und" e snr mae t more cloly approxia real da gag devces. e idues dcn bl dalng bhe nose and g lan f dvc f no e disbut ioduces sses n rang nban m dffn a f rfnc[6] Fna

since nr are and b he robo nvlvd lvng h cnc bwn an o p

st or van and aco wc wl aow m cngaeg f ifma

Tenan f gam ocur whe a va aelimad We dee a ap rgo whch deeaehow lose a puuer ome o lmna an va.Howeer, when nfrman i n a whch hva c b lca ll have aca nca.W mm q each ursuer w some m "h vad allng h bl f ncan obsan mak ss". P f aff cf he game ca clude

f usg rjci

dm

ig; ere adg cnv lclz va best dg bl

33 Infran Scure, Orga adCr

Th inng i hw h b e ld h am mmb a Eah a mmb m


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0

8

Figure 2 e PursuEvasio Game

me decss b avalabe fm, abu e

bes sg c ables us, e m member as a oca r scre ad a u cor decs le as ulned n So 2 T pe of form aaable - compleess ad de ngwh e uly ci dee hw an ge behaes fra xed se f cos We ve seccaly du n-formao scre ad decs rocesses a ase easy cmpared

Te a sce ca csis onl lcal nforma r c c fa cucad frme am members as we as compud rmao based bea sr e eece rdes he a-sc z e I pp wthe iormaon sc nd comps m u- whic s offered e icees or a am member coperate e tam he ssues ere voe ntgrang the team information, and he exac nat o the uity I epeimen we eithe use e eecue as foo eg d a lackd r rescro er w oca nfoon

Or experme dae have bee crlig edrecton o !ael of robot. Te dcsio meds aee d:

Pu lca aio d decisi

2 Compltey glbal formaton ad ne poo amu see pae 3)

A x o go nd oca infrmn with non

convex utty sucture

Te rs f ese s e ovus sagy casngwhaever s w he ewg adus an adng aybsacles he second amoun t the execuve chosngan evader t fol and the m memers geeg a llcoss hs has e undeable pope f te pursuers beng desoyed whie attepng purey goba obcives he

s med degas ceale cool by ng

ppor or team members cperate conbeeL The atr dsrgads idividual coces for gobabjecves, pssbly dsregdg mpr oca objectives

Te na med uses e execuve ntegrat inrmato abu evaders, and ffer a tam cetve t chase pcul ever But, aso le mem co nceve avd obsaes wh o e pat. g-ure 2 sws a tam cnguran were some eam members(hose labeed "D) e dsagreeg w e team n dero avod an obstle wle e res f te a (ld "PA) e llowing e exuves order ce an evader.his s our s expeence wh a of ca and gobacol

Ou e objecve in he simuaion s o consder nosybseratons d deveop sensr cono aorts Oidea for ths proect s te fllwng: reca that sensost distac and dcion W hncfrt e quantitis ae distribud accordng to some obby ds-ibun. he nfan scure f e eam onsstthe cuen bes esma and he oaton max o mum gd n Sco 2 e y ; a sensor w be the expeced chage n he inoa- fr e clses eader wn e cne f vs o imn nual m w ha orwhc ey ca cnbut the maximum noatn. Whae o deveoed any am poces for ts scenao yt

Evaluation and Specuatn

We ave consdered a very bac stac nonecusve asctur fo e sors ad cus o a oo syt. Trsuts tained or e aegaon o agnt opnons e tutively appeang and my very mp. lrl, e ol see pong Howev it s ceay e cae hat hemetods presend thus coud eay b deveoped w-

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out recours conc f eam nd nfoon suc-r have chos t iduce hse as fr manras sl as a vice ugh which he innsen snsr agnts y b easl expa, d condly beause we feel at am hec meodlgy has

gat pna f sg ad impemtig mrecmlex rganona sces a sysmac merur ma pi that em ei ni a ey -s nnpona fn f e plem nora mpuatina hiqe r agoi! no ei n t f a oheo[S] e ass e heent eem f cpaon d unc me thry e pppie ofr this clas of lem[ his sen icsses eadntages f e sess ises which n beexplo y

4 Inforatn an Structure

Many f e advngs f am eoc descpns si e ai yze effec f iffent gt eer) infan scs n e ve sysm capaiies R at e gnra case, e '" am me-e faon sc may wl epend n he thram membes acins ioao either a prefr-enc ng (nonrsive) or in e f a iaogue(rie) scte expe, consier a s caa an a tle sso, acg gher a a t iofn e ce a he s mahng agohm i icapal f ing dsptes and h dnsia caonsat e hn i the vew pe, whe a cte sesr nd on pp aw is best at nding ust suchhoizntal features In din it is reanabe assu

onl to e cra agnt, d t rsonse chaacrcsof actle snsor e of rleant only t e act conolr Th ambient llumnatn as meud y th caahas n releanc dison ade b te tcl nso nough ey may coopra dsabguang dgs allws infotion and con

esde

oiae y ruces proem cmlxty andeases peoce ptial We liee at ths s aci pipe for e nsct f inllgnt botcssye

is pin, we have nt scsd uncen n-fman Hweve, iatin om pctal sourcs s hav some scated uncta Unctns n dimensin y disusson of nfoaon we m e oe of blef n nfoao4]d hw hat shld ence the chice of on In thca f ct nsng foaton th aua orieae; and new infmatn c e derivd y usng consant f e plm t hd Henc nw acw eiher l foaon, or e rundant On

e her hd, faon is ncan adding or un-eated sans may not rea incre the avalablifan ultpe ea bseaons ay in factb a be sag snce that lkly duce ncn.Endg csdeatons such s pen pbli tam hoy, as ioaon scs prfct ca-pae f mdelg uncert ifoaton sourcs. Th hdustions hat se hw t sce the polng of nfaon ben snso w ndnt nforato how ke in the fe f ncity, an what controlmthods e ost apppa fr drecng e gang ofaton We e cuntly explorng thse ssus

ha, while a vsion systm s gd at fndng gob oca- 4.2 Loss Consideratins and Conrol

ons, a uch nsor is beer r rng bsrvans andresvig local aigus h s rd is shng Th ss uncon associatd wih an agnt or a a dof nfaton ben hese wo snrs: ei rspec teins th ssenia natur f dcson mer In ifan sctres shoud be made pndnt on each stdd optal conol foulaton spccaton of nor-othe's actns W c igne sfyig e pem so aton scts and loss od th citeia for eetionat th sln anng m a smple pmal n f e opi corol or sion re. Howvr oan exed iaogue ng pace been e w sesrs a ls often dicult dv ad hav a coursling ach the bservatons an ons, ving at tatonall coplx natu Gnral rsul kno onla nsensus disin aot the nvionment hs exaple for a resctd cs of infoaton sucturloss functonclely shws e advtages of a am theoc nass. fuatns. Ano mtho for slectng cnols s We can postulate alteae infoaton sctus for postulate clss of admssbe ro, and choose hseno and th dnmcs f e xcge of pons can mbr of hs clss hch nzs th loss. Lt be alzd Is a consnsus btand? Whn is a dcson can consdr consucng dcson rules d ho and alad Should councaton bddth ad or uatng th foranc rl an ojct basd ondrd? t sulaton studs In an cse, t chaactr of th loss

Anoher aspect of ts scenio is that h o snsos ncion s crucial in determining he esutant decision ehav arttond nnent a kind of "who ks or cono law.what nfoan suctr In gnrl not al th noa On ra hch nds o loaton s a thodoton abut a robocs syst s rlvat te cnscton ogy for scicaton of loss uncons Ideay, th ossof scc oons of t sys. Analogously all h functon should b justiabl n o obct craoraton aalable a snsors s not leant t pro rlatd to th robl. Pragatcal t s otn dct bance of al of sste In th xaple abv, t athacal conennce Fo th rctv orsatal chacerscs o cara ag of st or nds e don on tron o d oca


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loss caracterzations. Seon 2 presented some suls inthis deton, but mor work is surely need, paicullyn e case whe e am objectves e not expressible some combinaon of member's obectives

To illusa hat we have consid foulat-

ing loss ncons for onollng a sysm basd on a desrdstate of nforaon ha is, if the team h it goal somsate of nfoaton for exple, move is nformaton is nded, wha acton is ost appprate forproessing om he cuent infoaton s ward edesred nfoaon sta Should it slect on whichwill change e uncerta cia wi cunt informaon, or go ahead with an aon a dds uncoelatedevidence? How should it decide at it has enough nfor-maon? More concretely, should the execuve take otherpicture with e cama or phaps take a dierent view,or maybe use anoher sensor altogether Maybe e sensorshemselves should decide ndivdually what o do. Theseare al issues dealng wi the inracton of nfoaon andaction. By using am eory, we c ely foula hepoblem, specify loss functons or decision meods basedon, for exple, e petrs of a probabl dsbutonassociatd wi me nfoaton soure, and exane heresul via simulaon or by analytic methods.

4.3 Decson Theory nd

As we stated at the outse e consider our ork relevant toAI in that we t we may wt o consider information asintepreted, d would le consider p of a sysem asntellgent reonng agents n relad work dealng wi thenteraton of inteligent agen Rosenschen d Geneserethin 22] and Gnsburg in 9] have vestgad vaatons ongame eoretcal denions of rationa for coordnatng

intellgent agen. However ese sults e aempt analyze e neracon of elgent agen with a pirisuce and investigate he cnsequence of vaious raonaity sumptons We, on e oer hand, postula a giveneam scture and e ineresed n discoverng i pper-es. This is an mportant ndamental distction to keep inmnd

It is our vie hat owledgebsed reoning agenscan be used effetvely n e tam theoretic framework;but we must be able o describe them in terms of the ohersste elements tat is, as decision mes wih iformation suctures nd preferences about action In order toacieve this obecive, we must develop information suctures o be compatible wih A conceptions of information

as disree (usuall logical) toens d sehow conectconol suctures and loss foulations At his point wecan seh at least possibly. Frst view such reonngagen consisng of o phases: compung the infoaon sucure, and selecng an optimal action in he face ofavailable nfoation. This is similar o he clsic separa-ion o estimation in conol in conol theory literature3].Computation of e information stcture amounts o usingfuised infoaton and maing implicit information ex

plicit relave to a given moel23] That is, some pt ofte ifoation in the owledge be is used to infer nefacs om given informaton. The comple set of suc factsfo a limting snse) the nfoation scture Someof he eoretcal analyses of logical) noledge have de

led mthods for desrbg ths pcess of inference usingvaran of moda logic23] Loss foltions for e prefence of acons can be

spe using a conception of action simil to e sitatoncalculus17,19] n this sys acton is a mapping beweenworld ss, whe each state resen a conguraton ofhe orld Moo 19] has shown how bo nfaion action c be presend relaed itin e conceptuaamework of world staes, ming loss foulatons bseon information possible The actua details of is proceuree beyond the sope of is paper, but e can sho tatsevera proble i he planning domain can, in fact, bereduced decision proble posed n tis manner As aher example, consider building a decisionmaer whoatemp ll n gaps n an ncomplee, discree nowledge

base. The spcicaton of infomaion and loss functions canbe done in s of world staes as presened above, and eal mplemenation of the system done as a rulebasesystem.

inally, e may attmpt combine tis agent itagen which attempt to reduce uncertain n he probabilistic sense oulne e previous subsection For insance,a camera and a tactle sensor whch have local probabilisticuncertain reduction mehods, an a global executive icis building models of e envronment. Using team heoy,we c alze possible mehods for conol and cooperationof thes dispate agens d offer a coherent explanation ofhe fl system's behavior

5 Cncusns and Future Researc

Analsis of he general team organizaon it respect toam members inormation stctures provides a sysmaticework for addressing a number of iportant quesonsconceg the effect of sensor agent capabiiies on overal ssm perfoance. We sumze some of te moremporant issues

ould sensor benet b guidance om another ammember. hould comunication between membersbe increased.

2 Sould e sensors ability to me obseations be

enanced i ana, b canging e or nngalgoritmc botenecs

3 When would an exchange of opinions and dnacconsensus e atmpte

4 What overal sysem sucture (as described b einformation suctures of e tam members) is best(or beter) for different ass

7


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Siy, hee are a nube of mpot quesons thatc be addessed by alyzng he effec of individua membes ui d dision ncions, includng:

1 . Counicion d ime cos in e decision pocess pvide fo al me on

2. Inclusion of dcisions e new obseaons ofe envonmn if pevious opnions jec byohe membe, o if insufcin ifoaonwas ob on a s pass

3. Eec of new dision hescs on ovea sysmpefoce

Of course he ide may we be dicul considanalycally, ough is foaism does uc e schspace of aaives d pvides a amewok win whichese issues may be evalua The am heoeic oizaon is a powel mehod for alyzing muli-gen syse,

bu i is cely no he complee sweAcknowledgent: The Auho would e hak DMax Minz fo many vauable discussions abou is subject

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