Artificial Intelligence
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Artificial Intelligence Artificial Intelligence Document Transcript

  • Soft Computing (Immune Networks) In Artificial Intelligence Yasuhiko Dote Muroran Institute of Technology Mizumoto 27-1, Muroran 050 8585,Japan dote@,csse.muroran-it.ac.jp ABSTRACT iliis paper proposes a novel reactive distnbuted artificial usualh achieve theu tasks i n groups. llcactive a y i t s are sittiatcd~ntcIiigeiice (dvnaniic)using immune networks and other soft they do not take past e~eiits account. a i d can iiot Ibrcsee rlic intoLoiiiptitiiig inethods Fusth. extended sot? computing is defined ftiture. Their action is based on hat happens no. ho the ~ C I I ~ ~In .idding iiiuiiuiie networks and chaos theory including fractal distmzuish situations ui Ilie aorld. on the ~ a vthev resognveand ivavelet to conventional sott computing which is the fusion or world indexes and react accordingly llius. reacuve agents can notcoinbinatioii of tiizzv systenis.neural networks and genetic plan ahead what they will do But, what can be considered as a.~lgoritlinis and is suitable to cognitive distnbuted artificial weakness is one of theu strengths because the! do not ha^ to~ i i ~ c l l i ~ e n(static) Next, a novel fuuv neural net(genera1 ce revise their world model when perturbations chaiige the orld inparameter radial based function neural network) is developed in an tmepected u a ) Robustness and tatilt toleraiict arc t n o 01 theorder to use it for communication among agents in immune main properties of reactive agent swttiiis. j2 group of r e ; ~ c t ~ wiictnorhs The geiieral paraineter method is &ended to an agents can coinplete tasks even when one o l them b r d s doun.adaptive structured genetic algorithm to obtain much faster The loss of one agent does iiot prohibit the coinpietion o l theconvergence rate An unbiasedness criterion using distorter( a whole task, because allocation of roles is achieved locall bvradial based ftiiiction network i order to optimize parameters n perception of the enviroiunental needs. Thus, reactive ageiitresultiiy in die reactive distributed artificial intelligence hnd of svstems are considered as v: e flexible and adaptive because[ I 1(;MD!i) is applied to better generalization propertes. Then, t h s In this p a p e r ;I nozcaI re;ictive distributrtl ;irtifi(,i:ildeveloped Itvrv neural net is extended to a h g h performance int r llige nrr is proposvd us1 ti g Ish igrii ros i n i i i i i i ne 1. INTRODUCTION n r t w o r k ;11~[~r(~~1t~Iill;inli:(il ot1ic.r +oft rwmputin? :inc I<eactivit is a hehavior-bewd model of activitv,as opposed to approaches In section 11. soft coinpiit ing propoawl I)? xthe svmbol inanipulation model u.wd in planning.This leads to the L1r.L .i.Zadrh[-i] is r x t r n t l t d by :Ititling rhaos coinpiti ingiiotioii of cognitive c0st.i e.. the complexity of the over and iinmunr net,work theory. i novel fuzzy neural,Irchitecture needed to achieve a task Cognitive agents support a n r h v o r k with grneral p;lr;imrt.c-r statistics calculus takingcoinplz architecture which inems that their cognitive cost is advantages of both fiii.z ins arid neural iictnorhs i n sectionIiigIi.Copnitive agents have intenml representation of the world 111 In section IV this IS eaciided to a high perfonnoiice radial l i i ~ l iiiiiist he in adequation with the morld itself T h e process of hasis tiiiiction iieural iitturh using oii adaptive structure geneticrslating the tiitenial representation and the world is considered as algorithinl5 I close to the seneral parciiiieter iiicthod arid o kind 01I task On the other hand. reactive agents are simple. ~oinple- (iMnH[61. In section V these developed nctorks are applied tocash to uiiderstwd and do not support intemal representation of optiiiiize Ishiguros uimiune network reactive distributed artiticialthe world. Ilius. their cognitive cost is low, and tend to what is intelligence. cognitive economy. the property of being able to perfom~alledcvcn complsr actions with simple architectures Because of their 11. EXTENDED SOFT COMPUTINGcomplexit. cognitive agents are otteii considered as self- Soft coiiiputing is proposed bv I)r I* A./.adch/-ll to coiistrtict ~iitlicicrit the can nork alone or nith a ten other agents.0n die ne generation Ai [ macliiiie .intelligeiicc quatient) and to solvecoiitrm. reackive agcnts need companionshp Ihey can iiot work noiiliiiear and inatliematicallv iiimioJelld systems prohlenis isolated and they usually achieve their tasks in groups. Reactive (tractability) especiallv for cognitive artilicial intelligence In thisagents me companionship. They can not work isolated and thev section by adding chaos coiiiptituig arid iiiuiitiiie network thron. 198 $10.00 0 1998 IEEE0-7803-4778-1 , 1382
  • ilii extended soft computing is detined for explaining, what thev s t a r t i n g from t h e same initi;il conrhtions. In t h e firstcall. complex svstems(7). hunune networks are promising case. :I coiivcsiit ioniil I2HF" wiis uswl w i t h in~livi~Iu;ilapproachos to construct reactive artiticial intelligence[21 and [ 3 ]as adjusting of i t s vciights. I n t h e s;c.c:ontlc:ise. ( ~ l - l i l $ l ~ NIllustnltcd 111 Fig I was simuliitrtl with leiirning iilgorit hili (2) lh~, Hiinian I~eirig i k e .AI l vfficirnc? of both mrthorls w e r r ~ o n i i ~ ~ r byd using r ~~- Cogniriw t h e following m r a s u r e of convergrncr sprrd Fuzzy 1)istribur~d Systern ;I (Stnticl Fig.1 Soft computing in AI 111. NOVEL FUZZY NEURAL NET I J I irst. id consider the (iP approach to KHFN weights adjust~ng. Figure2. The simplest G P - R R F N ;s soon iis IIRI,N IS linear on its eights. the (iP method may bo impIementc.d in a straightfonxard manner The equation dcscribiiig (if-RBFN for a single output network is U IirrI, 1: . fisrcl initial v a l u e s of network we1ght.s: p: 0 400 800 1 si:;il;ir grnrr;il p a r a m e t e r t,o be adjusted with t h e fol I ow 1ng algori t hin Figure 3 Im;trning algorithm c:onvt!rgc!nc:o: ti) conventional IZUCN: 11) [;I-ltt3FN 1383
  • ~linic.nsioniilit> IeiirninC sprrtl of (;P-RBFS hitsiiicreasril reliitivelj- convent.iona1 RBFN.IitlFN to be used in adaptive fuzzy system ( A F S ) . incomnion case. is a s s u m e d to be t.ririned by m e a n s of t h e D(P: Q=-iiiiniiiiuiii necessary nunibrr of rules (hidden unitn u m b e r ) ilc.trrniin:ition a n d adjusting of t h e mean a n d vwtorh of iiithvidu:il hidd[,n nodes a s well asv;iri:incc~ Thereforr. the C: KHFNAFS Jetc,rniines Ih c , " t r u r " PthrJir eight5 In t h i s p:iper. t h e simplest CP RBFN fuzz! rulrj n u m b e r b; incrt~iiir~nt;lli! rwruii iny: I1 1 ~li;isc~tl :idapt ive fuzzy system for aut.oinatic fuzz!- rule r a k i i l basis fuiiction units ant1 cant inuous est i n i i i t ionniiinbrr tletemiination is proposed (Fig4). Only t h e of t.he approxlmtition quality through critrriii (4)nrtworli weights have been a s s u m e d t.o be adjust.ed by evwluat.ion for each fixed GP R B F S structure. T h ethe (:P algorithm while t h e c r n t r e s a n d widt.hs of unit network t o be determined is the network with 1r:ist+nsit I V P zon6.s yere ooiiipletel>- tleteriiiined w i t h the v:ilue oi i, anr! its unit n u m l ~ r rC :issiinic,~l h. I ton(,tworli input Gign;il r:iiigr :inil u n i t r,qu;il t o t h e fuzzy r d c ~ nuiiilwr C I ~c l i t * "s:lnililt~" iuzz> .ystc" Let consider t h e proposed procrdure in c1et.d for r h r siiiiplest case of t h e (P RBFN AFS Lvith : sciil;rr input 1 signal i n p u t slgniil II ( E : u := 0 ) iintl linovn nuni1ii.r of (:aussian units r] (for t h e first stage. y = I ) thr sensitive zone center coor&n;ites :ire calculiitcd by , relationship (5). CP KBFN BASED AFS - - ___ Figure4. G P RBFN adaptive furLy systemnuiiihrr during riich training rpoch whrrr. I is ii current unit n u m b e r For y = I ;incl I =I. "s:iniplr" fuzz!- systrm h a s been present.ed by RBFNUi t h I he "unknown" n u m b e r of hidden units (i.e.. fuzzy for rsiiiiiplr. o n r [tin recrivr (: = 0rules) Starting trom the single-unit-(;P-RBFN. thenr.twork learning h a s been performed by t h e scii1:ir 3 ) The initial (basic) sensitive zone w i d t h rqu;il ior allgrneriil piiriiiiieter iirljusting in the Learning netu.orli units I:, c;ilcul;itt.tl as ((5) blorli ~l r ~ ~ c c ~ ~ l u r ~ T h e stratly st iitr general p a r a m e t e r( ~ ~ ~it T I : I ion f<[fl ;ind viiriiince D { P ) have beenc,:ilcul:ittd hy GP Statist,ics Estimnt.or. The;~pproxiniationquality cnterion (1B) w a s evalutit.etl for( h p current (:P KRFN st.ructurr. rind decision onrh;inging o nrtwork structure p:ir:iiiirter iicljusting fiii t IIP 1,riiriiing Prow[iurr, L~lock. T h e stezicly s t a l egr~iii~riil p:ir:iniet<Jr ~ ~ s p e c t ; i t i o n E[P}antlviiriiince U ( P : have been c;ilculatrd by GP Stat.isticsl%timwt.or. The approx"t.ion quality crit.enon (1:3)viis n ~ : i I i i ; i t c dfort he current (:P RBFN st.ructure. and 1384
  • IC; p r t o r n i e d biised on input-out,put s a m p l e d;itii In this section. the 1JnbiasediiessCriterion tisiiig Distorter I I K I ) ) ;icwrtlingly to the ;iIgorithm ( 2 ) . Simiiltmeousl>- t h r approach( 8 I is used. which has been shon provldlng iiiiproved features in coiiipare to conveiitioiial methods. such as ~ k a i k e gcnrr;il p;ir:iiii~ter iJspwt;it ion E { P ) and viiriiince Infomiation Cntenon ( A I C ) [ 9 ] and its modification for neural networks Network Infonnation Criterion (MC) [IO], f i n l n i u m D[,& :ire estimated with some conventional method. Descnption Length (MDL)[ I I]. for rs-ample. by t h e movlng average calculation. Let consider the IJCD method application to the GP RBFN A F S The overall svstein block diagram IS shown ui Fig. 6.5 Both of them are (iP RBFN with a lemiing procedurs llie same signals are ted uito the network inputs The diiYerelici: I 111 the u a y of the teaclung signal usage While the reaching signill is fed mto uppa loop without any changes, the lower iietuork is trained by "distorted", i.e. nonliiirarly traiisfonned, sample d ~ t a The output of the lower network is also changed hv the transfoniier of the same transfer function as fir teachins sgiitl The critenon ol the iietuork structure optimality is derivedI61. nhich IS otthe tonnc 7) %ax (*,; (:2 :1 0 - 60-. C: umax I .( D = 5 /=I (U ) - I.-? (7 ) ] (7) IJiguref,. Definition of GP RBFN basic parameters where j-th set (vector) ofthe network input data. 17 overall c.v;iIii,ii NI :iii(I iiiemorizrtl. variables of the both networks. Tlie structure of the netnork n i t h the least value of the cntenon 7 1 is assumed to be a soliition ot the problem - :I ,- - 5 ... - :, , ,_ - :! , - ... : 8 ) The strucciirr of GP RHFN is modified by one inore .- . . . [:iiussiaii u n i t recruiting: y = q + l . T h e st.eps 1) - 6) :I r i a rvl)i.at 6.i I .Y , .VI The, r r s i i l t of t h e algorif hni 1 ) - 8 ) imp1ement;ition is :I Fig.6 Determinution of number of units by dibtorter 111 Iuiivtion u n i t s i n c:P IiBFS The proposed general paaiiieter method in scctioii Ill I , $1h i ( . h provic1c.s I he best :ipproxiniating accur:icy In the again illustrated in Fig.7.. This idear is extended to aii adaptive car ti is of fuzxy system theory i t iiieiiiis t h e fuzzy rule structure genetic algonthm[j]. Geiiotvpe has an adaptive ii ii m1)c.r clrtc~rminiitionproblriii solution. [8] structure . The string representation is constructed by two l a y s 1V 1 IJ(il.1 PERFOIWANCE RI3k.N One is nanied locus l a y . the other .operon l+er as slio!!ii iii lhe prohiein of the reliahiliiy n1 the denved model is one of thc IFig 8 For this reprcseiitatioii .live ne genetic o~)er~i~i~iiis iirsiiiost iiiipottaiit ones. ansing duruig the identitication task solving detined in order to scll~orgaiii/t:the siring itriicture and dsvclo1)Hic model over-titting prevention IS a crucial point tor inam adaptive genctic change 111 the evolutioiial proy l c t i c a l iinplrmeiitations 11s i t WJS discussed ui the preceding approach bnngs attractive optiiiiiimoii results fbr probizinssections, there are several approaches to cope with this ditficultv including (iA-dilticultv.Suice genetic algorithm and chaos 1385
  • Loinputnips are heuristic approaches, they have capabilities of a fashion.Namelv.onlv one antibodv is allowved lo activate and act creative thinking ivav or evolution its corresponding its action to the ivorld 11 its coiiceiitratioii H i these techniques the Iuzzv neural net in section III turns Into surpasses the prespecitied the threshhold As shovii in Fig10 . ilic <I high pcrlbnnancr radial basis fuiictlon neural network concentration of the aiitibodv is influenced b the stimulatioii iuid ! Fig.7 General parameter method suppression from other antibodies . the stiiiiulation froin antigeii. String and the dissipation Factor t i c. natural death ). The concentration 01 I-th antibody .which is denoted by a, . is calculated b ( X ) ! (I and 0 are the rate of interaction ainong antigens and antibodird. +. ..... .... ~ a l u elist t i x e d lenzth + .~ ........ _ - ~ _ Locur libel V V ............ ~ .~ ...... V General Parameter . . _ ~ ~ - ... .. .. .. ....... ....... ...... N!:eight layer (fixed nominal value) -. __ ..- /I;, ... -- ......... Ili,, - li:, ....... II, ... It,;," _- r .---.; * -.: *. 1 ...... ...... ? -- i ~ _ _ _ ~ : - ~-~ -~ . . . ._. . ~ ~ Inputs : blutually Inputs : Mutually Correlated Correlated_ _ - I . . - .... - ... I .. -- Fig.8 Adaptive string structure o f genetic algorithm N N N V. SOFT COMPUTLNG I REACTIVE N tlA,(tvdt=( (L ( XI11 il (1) XI11 ) n i Llll .<I. ( 1 ) DISTRIBUTED ARTIFICIAL J-I 1 1 k 1 INTELLIGENCE I Is1l l G [ J R O 3 REACTIVE IIISTRIBIJTED ARTIFICIAL X IN:, - 0 111: k. ~ ii: (t) (8) INTEI.Ll(;ENCE WITH M J E NETWORKS[Z] and [i] MN k=I ihe detected current situation and competence modules as il. ( t - I ) -1.. (l.rxp(O. 5 - A . ( t ) ) ) .iitigciis and Antibod~es,respzctiveI~ liere N IS die number of antibodies. a i d nil denotc~inatclinis lo inake a iinonoido(antihody) select a suitable antibodv against ratio hrtneen antibod! I and antigen .m), denotes dcgrce 01 that ilw wrreiit antigen, it IS highlv important I i o ~ the antibodies disalloance of antibod I for antibod! I The first and sccond arc described .Moreover.it is noticed that the unmunogical tenns of nght hand side denote the stiiiiulatioii and supprzssioti dntration inecliamsm select an antibody in bottom up manner by from other antibodies, respectively The thrd tenii represents lhr ~ommuiiicating aiiioiig the antibodies. To rwlize the above stimulation from antigen, and the forth tenn thtl natural death -~ . . ~ _ _ _ ~ - . rcquireineiits. the descnptioii the description of antibodies are -7zEED Idiotour defined as follons The identitv of a specific antibody is generally . . . ~ ~~ ilcleniiinzd h? the stncture of its paratope and idiotope F i g 5 dcplcts thc represetitation of antibodies As shown iii this tigure.a pair of precondition action t o paratope .the nuinher of ll~wllord antibodies and thc degrce ot disallowance to idiotope ,irc respectively assigned In addition, the structure of paratope is Food Bark Middle Hwkwud Obsmclr I vtl FW KlEhi J I ided into four portions: objects, direction,distance, and action. EnrrgY _ i.cn and c , r . . - ~ ni>d et, For adequate selection of antibodies . one state variable called Fig.9 Represent;rtion of antibodies concentration is assigned to each antibody. The selection of ;Ilitibodics IS simply carried out i n a wiimer-take a l b 1386
  • hi order to optimize this reactive distributed artificial intelligence. Heunstic Model Selection Cnterion I king Distorter andh e deve1opr:d ftiziv neural net is applied to communication Its Application to Detenmiumatioii of the Nuinher oIaiiioiig agents( antigens and antibodies ) The developed radial Hidden IJIUIS in RBFN, .louiial o t rhr: .lap Soc 01hasis function neural net is used to optimize parameters in (8) and Syt.Contr. and Inf.,Vol Il,N0.2,l99X.pp6 1-70lbr a inetadyaniics whch produces and removes antigens and Y.Dote,"Sott Coniputmg( Immune Networks) 111ailtibodies to make reactive tables.[f] Artificial Intelligence". Web.site:http-//bik.csse Muroraim.Japan. I997 VL. CONCLUSION D FhE;hntetov.Y.Dote and M S ShaiMi."Sstriii 1111s paper proposes extaidtxl sott computing to construct 10% Identilicetion bv the (iciieral lurumeier Netdcos^ reactive distrihuted artificial intelligence resutmg in excellent Netuorks nith Fuzzy self-or~anizaiion"frep. o t the I I"decision iiiahng. Table IFAC SVmP on SvsrelllI shows the comparison of the proposed system vvith fuzzy I 997.~~829-8.34 IdentiIication,Kitak~shu,Japaii,Vol.2,svstems on decision making. H.Al;aike."A New Look at the Statistical Model Ideiiti!ication".IEEE Tran. On AC.Vol 19.I974.pp71b Tirblel Comparison of immune network- 72 3 based with fuzzy reiisoning approach M.Murata.S Yoslukava uid S.Aiiian."Nt.r~orL Infonnation Cntenoii-l)eieniuiuimg die Nuinher ol Iiiiiiiuiic iietnork-bawd Twn reasoning Hidden IJiUts for Anilicial Neural Nelnork t3ottoiii-up decentralized Top-dow~ centralized Model".IEEE Tran. on Neural IIsplicit uiteraction Implicit interaction 1)viiamir: static Net,Vol.j,No.j, I994,pp865-872. J kssanen,"A IJniversal Prior tor Integers and REFERENCES Estimation bv M " u m Descriptloii I .engtIiC. Annals 01 _1 lcrhcr."Reactive I)istnhwed Arti ticial Statistics.Vol I I.No 12.l9X3.pp4l(~-i.~l Intt.lli~eiice.Principles and Applications".Chapter I I .I:oundations of Distnbuted Artificial hitelligence,cdited bv GM.P.Oharc and N. R.Jemngs,John Wilev&Sons Innc.,New York, 1 9 9 6 . ~ ~ 2 8 7 - 3 14. and Y.lJchkava."ki A.Ishiguro.T.Kondo.Y.Watanabe Iniiiiunogical Approach to Behavior Control of .~lutoiioimious Mobile Ilobots-Coiistructioii Immune Netuorks Through Leanimg--.Proceedmgs of the hitexnational Workshop on Solt Computing in Industry( IWSCI96),Muroran,Jap~i.April17-28.l996,pp 253-267. A 1shiguro.YWatanahe.l:Kondo and Y I Ichil;aa."Constrctioii of a Decentralized Consensus-Maklng Netaork Based on the Inunune S stem-Application lo Action Arbitration for an ! Autonornous Mobile Robot-",The SlCE Trans. .Vol.33,No.h. I097,pp 524-5.32(inJapanese). I. A %adeh?The Role of Soti Computing mid Fuzzv Logic iii the Conception.Design. Development of 1111211igeiit Svstems".€roc Of the I990 PP I .3b- IWSC IOh.Mtiroraii.Japaii.ApnI27-ZX. I .37. (Ileiian: Speaker) IC Ohkura and K.11eda..Srlf-Orgaiii/;ing of Stnng Structure arid Adaptive < imetic Swrch".Proceedings of the IWSC 106 pp 172- I77 t r TaLeuclu and T Mpos1ii.H Ishihashi and H.Tanaka,"A 1387