a brief introduction to software agent technology

8/2/2019 A Brief Introduction to Software Agent Technology

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Section 1

Sof tware A gents Co ncepts


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An Introduction to Agen t Technology

H S Nwana and D T Ndumu

Intelligent Systems Research, Advanced Applications & Technology Department,BT Laboratories, Martlesham Heath, Ipswich, Suffolk, IP5 7RE, UK.

E-mail: hyacinth/[email protected]

Intelligent agent technology is a rapidly developing area of research.Howe ver, in reality, there is a truly heterogeneous bod y of work beingcarried out under the 'agent' banner. In this paper, software agenttechnology is introduced by briefly overviewing the various agent typescurrently under investigation by researchers.

1 . I n t roduc t i on

The word 'agent' is currently in vogue in the popular computing press and within theartificial intelligence (AI) and computer science communities. It has become abuzzword because it is both a technical concept and a metaphor. However, its rampantuse could conjure up the problems faced with other flamboyant titles including'artificial intelligence' itself; far too ambitious claims precede the real technical workthat follows.

This paper presents the real challenges and potential bene fits o f agent research. T hemain goal is to overview the rapidly evolving area of software agents; the overuse of

the word 'agent' has tended to mask the fact that, in reality, there is a trulyheterogene ous b ody of research being carried out under this banner. This paper placesagents in context, defines them and then goes on to overview critically the rationales,hypothese s, goals, challenges and state-of-the-art de monstrators of the various agen ttypes currently under investigation. It also proceeds to overview some other generalissues which pertain to all the classes of agents identified.

2 . S o f t w a r e A g e n t s - - H i s to r y a n d t h e C o n t e x t o f t h is P a p e r

Arguably, software agents date back to the early days o f AI work, indeed, to CarlHewitt 's concurrent actor model [1]. In this model, Hewitt proposed the concept of aself-contained, interactive and concurrently-executing object which he termed an'actor'. This object had some encapsulated internal state and could respond tomessages from other similar objects.


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Along wi th d i s t r ibu ted prob lem so lv ing and para l le l AI , sof tware agents andmu l t iagen t sys tems (MA S) fo rm co l lec t ive ly one of the th ree broad a reas which fa l l

under d i s t ribu ted AI (D AI) . Hence , they inher i t m any o f D A I ' s mot iva t ions , goa ls andpoten t ia l benef it s , fo r example , m odu lar i ty (which reduces comp lex i ty ) , speed (due toparal le l ism), re l iabi l i ty (due to redundancy) and f lexibi l i ty ( i .e . new tasks arecom posed mo re eas i ly f rom the m ore m odu lar o rgan isa t ion) . They a l so inher i t benef i t sf rom A I such as opera t ion a t the know ledge level , easie r main tenance , reusab i l ity andpla t form independence [2].

Bro adly, for the purpo ses o f this paper, the research o n ag ents is spl it in to tw ogenera tions - - the fi r s t spann ing the per iod 1977- -199 0 , and the second from1990 tothe cur ren t day. F i r s t genera t ion wo rk on agents concent ra ted main ly on de l ibera tive-

type agents wi th sym bol ic in te rna l mo delsL Par t icu la r ly, they concent ra ted on macroissues such as the in te rac tion and com m unica t ion be tw een agents , the decom pos i t ionand d i s t ribu t ion of t asks , co -ord ina t ion and co-operat ion , and conf l ic t reso lu t ion v ianegot ia tion . These 'ma cro ' aspec ts o f agen ts emp has i se the soc ie ty of agen ts overind iv idua l agen ts . The goa l was to spec i fy, ana lyse , des ign and in tegra te sys temsconsis t ing o f m ult iple co l laborat ing agents . Chaib-draa e t a l [3], Bon d and G asser [4]and G asser and Huh ns [5] p resen t exce l len t rev iews o f wo rk on the f i r s t genera t ion o fagents . I t is im portan t to no te that th is w ork s t i ll progresses .

Second genera t ion work o n agents i s v iewed as be ing charac ter i sed by two m ajorand d i s t inc t s t rands - - research and deve lopm ent o f agen t theor ies , a rch i tec tures andlanguages , and a s ign i f ican t b roadening of the ty polo gy of agen ts be ing inves tiga ted .The form er research is wel l sum mar ised in W ooldr idge and Jennings [6 , 7 ] andW ooldr idge e t a l [8 ]. This paper com plements them by concent ra t ing on ov erv iewingthe broadening typo logy o f agen ts be ing inves t iga ted by agent researchers .

3. Agen t Ap plicat ions

The range o f f 'nar is and un ivers i t i es ac t ive ly pursu ing agen t t echnolog y i s qu i te b roadand the l is t i s ever g rowing . I t inc ludes smal l non-ho useho ld names , m edium-s izeorganisa t ions and the la rge mul t ina t iona ls. The scope o f the appl ica t ions be ingdeve loped is a rguably more impress -ive - - i t r ea l ly does range f rom the mun dane tothe modera te ly ' sm ar t ' . To ward s the smar t end of the spec t rum are the l ikes o f Sy cara ' sv i s i to r hos t ing sys tem [10] a t Carnegie Mel lon Univers i ty (CMU). In th i s sys tem,agents co-opera te in o rder to c rea te and m anag e a v i s i to r ' s schedule to CM U.

To ach ieve th i s , the agents f i r s tly access on- l ine in form at ion resources in o rder todetermin e the v is i tor ' s nam e, organisat ion, s ta tus in their organisat ion, areas of interes tand pro jec t s be ing worked on . Secondly, us ing the in format ion ga thered about thev is ito r, they re t r ieve in format ion (e .g . rank , t e lephone nu m ber and e -mai l address ) f rompersonne l da tabases in o rder to de te rmine appropr ia te facu l ty mem bers to m ee t thev is ito r. Thi rd ly, the v i s i to r hos t ing ag ent com poses m essages w hich i t despa tches to the

1A deliberativeagent s "one tha t possesses an exp licitly epresented, symbolicmodel of the w orld, and inwhich decisions(for example about what actions o perform)are made via symbolic easoning"[9].


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ca lendar agen t s o f these facu l ty me mb ers , a sk ing w he ther they a re wi l l ing to mee t th i sv i s i to r and a t wh a t t ime . N ext , the responses a re co l l a ted and the v i s i to r hos t ing agen t

c rea tes the schedu le fo r the v i s i to r which invo lves booking rooms fo r the va r iousappoin tments wi th facu l ty members . Na tura l ly, the sys tem in te rac t s wi th the humanorganiser, seeking conf i rmat ion, refuta t ions , suggest ions and advice .

Appl ica t ion doma ins whe re agen t so lu t ions a re be ing app l ied o r resea rched inc ludewo rk- f low ma nagem ent , ne two rk manage me nt , a i r- tr a ff ic con tro l , bus iness p rocess re -eng ineer ing , da ta min ing , in fo rmat ion re t r i eva l /management , e l ec t ron ic commerce ,educat ion, personal d igi ta l ass is tants (PDAs) , e-mai l f i l ter ing, d igi ta l l ibrar ies ,com ma nd and con t ro l , smar t da tabases , schedu l ing /d ia ry mana gemen t . Indeed , a sGui l foy le no tes :

" . .. in 10 yea rs ' t ime m os t new IT de ve lopm ent wi l l be a ffec ted , and m anyconsu me r p roduc t s wi l l con ta in e mb edde d agen t -based sys tems ." [ 11 ]

4 . W h a t i s a n A g e n t ?

The re is a s m uch chance o f agree ing on a consensus de f in it ion fo r the wo rd ' ag en t ' a sthe re i s o f AI resea rchers a r r iv ing a t one fo r ' a r t if i c ia l in te l l igence ' ! W hen ne cessa ry anagen t is de f ined as re fe r r ing to a com pone nt o f so f tware and /or ha rdware which iscapab le o f ac t ing exac t ing ly in o rder to accompl i sh t a sks on beha l f o f i t s use r.Ho we ver, i t wo uld be p re fe rab le to say i t i s an um bre l l a t e rm w hich covers a range o fo ther m ore spec i f i c agen t types , and then go o n to l i s t and def ine w ha t these o ther agen ttypes are .

4 .1 A Typ o logy o f Agen t s

There a re severa l d imens ions to c lass i fy ing ex i s t ing sof tware agen t s . F i r s t ly, agen t smay be c lass i f i ed by the i r mobi l i ty, i . e . by the i r ab i l i ty to move a round some ne t -work - - th is y ie lds the c lasses o f st a ti c o r mo bi le agen ts . Secondly, they m ay bec lassed as e i the r de l ibe ra t ive o r reac t ive . De l ibe ra t ive agen t s de r ive f rom thede l ibe ra t ive th ink ing parad igm which ho lds tha t agen t s possess an in te rna l symbol icreason ing mode l , and they engage in p lann ing and nego t ia t ion wi th o ther agen t s inorder to ach ieve the i r goal s. W ork on reac t ive agen t s o r ig ina tes f rom resea rch ca r r i edou t by Broo ks [12] and Agre and C hapm an [13]. These agen t s do no t have any in te rna lsymbol ic mode ls o f the i r env i ronm ent , and they ac t us ing a s t imulus / response type o fbehav iou r by respond ing to the p resen t st a te o f the env i ron me nt in which they a ree m b e d d e d [ 14 ].

Th i rd ly, agen ts m ay be c lass i f i ed a long severa l a t t r ibu tes which idea l ly they shou ldexh ib i t. At BT Labora to r ies , a min imal l is t o f th ree has been iden t i fi ed - - au tonom y,lea rn ing and co-opera t ion . Au tono m y re fe r s to the p r inc ip le tha t agen ts can opera te onthe i r ow n wi thou t the need fo r hum an gu idance , even though th i s would som et imes beinvaluable . A ke y e lem ent of autono m y is proac t iveness , i .e . the abi l i ty to ' ta ke theini t ia tive ' [6] . Co -ope rat ion w i th other agents i s para m oun t - - i t i s theraison d'etre fo r


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hav ing mul t ip le agen t s . Fur the r, the communica t ion requ i red to ensure co-opera t iongenera l ly invo lves h igh- leve l messages . T he use o f h igh- leve l me ssag ing l eads to lowe r

com mu nica t ions cos ts , easy re - implem entab i l i ty, and concu r rency. Las tly, fo r agen t s tobe t ru ly ' smar t ' , t hey would have to l ea rn as they reac t and /or in te rac t wi th the i rex te rna l env i ronment , so tha t , wi th t ime , the i r pe r fo rmance inc reases . These th reecharac te r is t ic s a re used in F ig 1 to de r ive th ree types o f agen t to inc lude in th i stypo logy - - co l l abora t ive agen t s , in te r face agen t s and t ru ly smar t agen t s .

autonomouss o f t w a r e

co ope a - - ~ ' ~ ~ 1 - " - - - ' ~ e a rn

| i a g e n t s I lcollaborative ~ ] in t e r f a c e /

s y s t e m s

Fig. 1. A part view of an agent typology.

I t i s emphas i sed tha t these d i s t inc t ions a re no t de f in i t ive . For example , wi thco l labora t ive agen t s , the re i s m ore emp has i s on co -opera t ion and au tono m y than onlea rn ing ; bu t i t i s no t impl ied tha t co l l abora t ive agen t s never l ea rn . Anyth ing e l sewhich l i e s ou t s ide the ' c i rc les ' i s no t cons idered to be an agen t . For example , mos texper t sys tems a re l a rge ly au tonom ous ; b u t , typ ica l ly, they do no t co-opera te o r l earn .Idea l ly, agen t s shou ld do a ll th ree eq ua l ly w e l l, bu t th is i s the asp i ra t ion ra the r than therea li ty. Tru ly sm ar t agen t s do no t y e t ex i s t.

Four th ly, agen ts m ay som et imes be c lass if i ed by the i r ro les (pa r ti cu la rly, i f ther o le s a r e m a j o r o n e s ), e .g . W o r l d W i d e W e b ( W W W ) i n f o rm a t i o n - g a th e r i n g a g e nt s.Es - sen t ia l ly, such agen t s he lp ma nage the vas t am oun t o f in fo rmat ion in wide a reane tworks l ike the In te rne t. Th i s c lass o f agen t i s r e fe r red to as an in form at ion o rIn te rne t agen t. Aga in , in fo rma t ion agen t s m ay b e s ta t ic o r mob i le and de l ibe ra t ive o rreact ive .

F i ft h ly, a ls o i n c lu d e d i s th e c a t e g o r y o f h y b r i d a g e n ts w h i c h c o m b i n e tw o o r m o r eagen t phi loso phies in a s ingle agent .

There a re o ther a t tr ibu tes o f agen t s w hich a re cons idered seco ndary to those a l ready

ment ioned . For example , i s an agen t ve rsa t i l e ( i . e . does i t have many goa l s o r does i tengage in a va r ie ty o f t a sks )? I s an agen t benev o len t o r non-he lp fu l , an tagon is ti c o ra l t ru i s t i c? Does an agen t l i e knowing ly o r i s i t a lways t ru th fu l? I s i t t empora l lycon t inuous? Perhaps unbe l ievab ly, some resea rchers a re a l so a t t r ibu t ing emot iona la t ti tudes to agen t s - - do they ge t ' f ed u p ' be ing asked to do the same th ing time andt ime aga in [15]? Som e agen t s a re a l so im bue d wi th m enta l is t ic a t t itudes such as be li e f s,


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desi res and in tent ions [16, 17] . Such a t t r ibutes as these pr ov ide for a s t ronger de f ini t iono f a g e n th o o d .

In essence , agen ts ex i s t in a tru ly mu l t i -d ime ns iona l space . H ow ever, fo r the sakeo f c la r i ty o f unders tand ing , th i s space has been ' co l l apse d ' in to a s ing le l i s t o f s ix typesof agen t:

9 col laborat ive;

9 in terface;

9 mobi le ;

9 in format ion / In temet ;

9 react ive;

9 hybr id .

T h e r e a r e s o m e a p p li c at io n s w h i c h c o m b i n e a g e n t s f r o m t w o o r m o r e o f t h e s eca tegor ies, and these a re re fe r red to as he te roge neous agen t sys tems .

5 . A P a n o r a m i c O v e r v i e w o f t h e D i f f e r e n t A g e n t Ty p e s

This sec t ion con ta ins an overv iew o f al l the types o f agen t s iden t if i ed in the typo lo gy ofthe p rev ious sec t ion in t e rms o f some or a l l o f the fo l lowing - - the i r e ssen tia lmetaphors , hypo theses /goa l s , mot iva t ions , ro les , p ro to typ ica l examples , po ten t i a lbenef i t s , and key cha l l enges . Th i s overv iew does no t inc lude smar t agen t s on thegrounds tha t th is i s the asp i ra tion o f ag en t resea rch ers ra the r than the rea l ity.

5.1 Collaborative Agents - - An Overview

As shown in F ig 1 , co l l abora t ive agen t s emph as i se au tonom y and co-opera t ion wi tho ther agen ts in o rder to pe r fo rm tasks fo r the i r owne rs in open and t ime-cons t ra inedmul t i -agen t env i ronments . They may lea rn , bu t th i s a spec t i s no t typ ica l ly a majoremphas i s o f the i r opera tion , though som e per fo rm l imi ted param et r i c o r l ea rn ing byro te . To co-ord ina te the i r ac t iv i t i e s , they may have to nego t ia te in o rder to reachmutua l ly accep tab le agreements . Co l labora t ive agen t s t en d to b e s t at ic , l a rge , coarse -gra ined agen ts . T hey m ay be benevolen t , r a t iona l , t ru th fu l , some com bina t ion o f theseor nei ther. M ost o f the w ork c lass i f ied in th is pape r as f i rs t generat ion inv est igated th isc lass o f agent . As no ted ea r li e r, som e resea rchers a re p ro v id ing s t ronger de f in i t ions to

such agen ts , and , a s a resu l t, the c lass o f co l l abora t ive agen t s m ay i t se l f be pe rce ived asa b road g roup ing .


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5 .1 .1 H y p o t h e s i s , M o t i v a t i o n a n d B e n e f i t s

The ra t iona le fo r hav ing co l l abora t ive agen t sys tems i s a spec i f i ca t ion o f the goa l o fDA I . I t may be s t a ted as: " . .. c rea ting a sys tem tha t in te rconnec t s separa te ly deve lopedco l labora t ive agen ts , thus enab l ing the ensem ble to func t ion beyo nd the capab i l it ie s o fany o f it s me mb ers" [2] . Some o ther mot iva t ions fo r DA I resea rch , and henceco l labora t ive agen t resea rch , no t a l ready m ent ioned , inc lude :

9 solving proble ms that are too large for a centra l ised s ingle agen t to do due toresource l imi ta t ions o r the sheer r i sk o f hav ing one cen t ra l i sed sys tem;

9 a l lowing fo r the in te rconnec t ing and in te ropera t ion o f ex i s t ing l egacy sys tems , e.g.

exper t sys tems , dec i s ion suppor t sys tems , co nven t iona l p rograms , e tc ;9 providing solut ions to inhere nt ly dis t r ibuted prob lem s, such as solut ions w hich

draw f rom d i s t r ibu ted in format ion sources such as d i s t r ibu ted on- l ine in format ionsources o r d is t ribu ted sensor ne tworks (e .g . D V M T [18]) , and so lu tions where theexper t ise i s d is t r ibuted, such as in heal th-care provis ioning or a i r- t raff ic control(e .g . OA SIS [17]) .

5 .1 .2 A P r o t o t y p i e a l E x a m p l e - - T h e P l e ia d e s S y s t e m

The Ple iades proje ct [19] appl ies col lab orat ive agents in the dom ain o f organisat ionaldec i s ion making o ver the ' in fosphe re ' (which re fe r s essen t ia l ly to a co l l ec t ion o fIn te rne t-based he te roge neous resources ) .

Ple iades is a d is t r ibuted col laborat ive agent-based archi tecture which has twolayers o f abs t rac t ion - - the f i r s t l ayer con ta ins t a sk-spec if i c co l l abora t ive agen t s andthe sec ond in format ion-spec i f i c co l l abora t ive agen ts ( see F ig 2 ) . Th i s a rch i t ec tu re wasused to d eve lop the v i s ito r hos t ing sys tem w hich was desc r ibed ea r li e r. Task-spec i f i cagents (TA) perform a par t icular task for thei r users , e .g . ar ranging appointments andmeet ings wi th o ther t a sk agen ts . These agen t s co-ord ina te and sched u le p lans based on

the con tex t . They co l l abora te wi th one ano ther (wi th in l ayer 1 ) in o rder to reso lveconf l i c ts o r in tegra te in format ion . In o rder to ga rner the in format ion requ i red a t th isl eve l , they reques t in fo rmat ion f rom in format ion-spec i f i c agen t s ( IA) . In format ionagents , in turn , may col laborate wi th one another ( i .e . wi thin layer 2) in order toprov ide the in format ion reques ted back to the l ay er 1 reques t ing agen t. The sources o fthe in format ion a re the many da tabases (DB) in the in fosphere . Ul t imate ly, the t a skagents prop ose a solut ion to thei r users .

Ta s k a g e n ts e n c o d e a m o d e l o f t h e ta s k d o m a i n a n d k n o w l e d g e o f h o w t o p e r f o r mtasks , as we l l as an acqu aintance m od el deta i l ing the capa bi l i ties of o ther task or

in format ion agen ts . The y a l so possess some lea rn ing mecha n isms . In format ion agen t spossess know ledge o f the va r ious in format ion sources and ho w to access them, and anacqua in tance mode l spec i fy ing the ab i l i t i e s o f o the r in fo rmat ion agen t s [10] .Individual ly, an agent consis ts of a p lanning m odu le l inke d to i ts local bel iefs and factsdatabase . I t a lso has a local scheduler, a co-o rdinat io n m odu le and an exe cut io nmoni tor. Thus , agents can ins tant ia te task plans , co-ordinate these plans wi th other


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agents and schedule/monitor the execution of their local actions. Interestingly, thearchitecture has no central planner and hence agents must all engage in co-ordination

by communicating to others their constraints, expectations and other relevantinformation.

so lu t i on

asg layer2 > ( - ~

layer 1

r e p l y ~

Fig. 2. The Pleiades distributed system architecture (adapted from Sycara [10]).

5.1.3 A Brief Critical R e v i e w o f C o l l a b o r a t i v e A g e n t S y s t e m sWork

There are many other useful pieces of work on collaborative agents. At BTLaboratories, two prototype collaborative agent-based systems, ADEPT and MII, havebeen developed recently. ADEPT [20] employs collaborative agents in the applicationarea of business process re-engineering while MII [21] uses collaborative agents toperform decentralised management and control of consumer electronics, typicallyPDAs or PCs integrated with services provided by the network operator.

The key criticism of collaborative agents levelled by some researchers stems fromtheir grounding in the deliberative thinking paradigm. Researchers in the reactiveagents camp argue that this results in brittle and inflexible demonstrators with slow

response times. In section 5.5 the deliberative versus reactive agents debate is brieflydiscussed.

5 . 1. 4 C o l l a b o r a t iv e A g e n t s - - S o m e K e y C h a l l e n g e s

Despite successful demonstrators like the Pleiades system and ADEPT, collaborativeagents have been deployed in few real industrial settings, e.g. the ARCHON project[22]. There are still many teething problems.

Engineering the construction of collaborative agent systems - - t o paraphrase B T's

Professor Robin Smith: 'We must move away from point solutions to pointproblems, and design methodologies which allow for quicker, more structuredimplementation of collaborative agent-based systems'.


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10

In te r-agen t co-ord ina t ion - - co-ord ina t ion is e ssen t ia l to enab le g roups o f agen tsto so lve p rob lems e ffec t ive ly because o f the cons t ra in t s o f r esource boundedne ss

and t ime . W i thou t a c lea r theory o f co-ord ina t ion , anarchy o r dead lock can se t ineas i ly in co l l abora t ive agen t sys tems . M uch wo rk i s r equ ired to address the i s suesof co-ord ina t ion and nego t ia t ion .

S tab il ity, sca lab il ity and per fo rm ance i s sues - - inves tiga tions nee d to be ca r r i edou t to es tab l ish su i t ab le m in im um leve l s o f pe r fo rman ce and , c lea r ly, thesesys tems have to be show n to be s t ab le .

Learn ing - - wh a t a re the appropr ia te lea rn ing mec han isms fo r d i ffe ren t types o fprob lems? Would l ea rn ing no t l ead to ins tab i l i ty? How do you ensure an agen t

does not spen d mu ch of i ts time learning, ins tead o f par t ic ipat ing in i ts se t -up?

Eva lua t ion o f co l l abora tive agen t sys tems - - how a re they ver i f i ed and va l ida tedto ensure they mee t the i r func t iona l spec i f i ca t ions? Are unan t ic ipa ted even t shand led p roper ly?

In conclus ion, despi te the cr i t ic isms of col laborat ive agents , there are manyindus t r i a l app l ica t ions which would benef i t s ign i f i can t ly f rom them. For example , apo ten t ia l major ro le i s seen fo r them in bus iness p rocess manage ment .

5 . 2 I n t e r f a c e A g e n t s - - A n O v e r v i e w

In te r face agen ts ( see F ig 1 ) emphas i se au ton om y and l ea rn ing in o rder to pe r fo rm tasksfor the i r owners . Maes [23] po in t s ou t tha t the key m etaphor und er ly ing in te r faceagents i s that of a person al ass is tant w ho is col laborat ing wi th the user in the sam e w orkenvi ronment . Note the sub tle emphas i s and d i s t inc t ion be tween co l l abora ting wi th theuser and co l laborat ing wi th other agents as i s the c ase wi th c ol laborat ive agents.

Essent ia l ly, in terface agents sup port and pro vide p roact ive ass is tance, typica l ly to auser learning to use a par t icular appl icat ion such as a spreadsheet or an operat ing

sys tem. Th e agen t obse rves and m oni to r s the ac t ions t aken by the use r in the in te r face( i. e. 'w a tches o ver the sho u lder o f i ts use r ' ) , l ea rns new ' shor t -cu t s ' , and sugges tsbet ter ways of doing the task. A s fo r learning, typical ly , in terface agents learn to ass is tthe i r user s in the fo l lowing four ways :

9 by observin g and imita t ing the user ;

9 th rough rece iv ing pos i t ive and nega t ive feedba ck f rom the use r;

9 by rece iv ing exp l ic i t ins truc t ions f rom the use r ;

9 by ask ing o ther agen t s fo r adv ice .

Genera l ly, the l ea rn ing modes a re memory-based l ea rn ing by ro te o r pa ramet r i c ,though o ther t echn iques such as evo lu t ionary l ea rn ing a re a l so be ing in t roduced . The i rco-op erat ion wi th o ther agents , i f any, is l imi ted to asking for advice .


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M aes [24] specif ies two precon di t ions to be fu lf i l led by su i table appl icat ionprogram s for interface agents - - f i rs tly, that there is substant ia l repet i t ive beh aviou r in

us ing the appl ica t ion (o therwise , the agent wi l l no t be ab le to l ea rn anyth ing) , and ,secondly, tha t th i s repe t i t ive behaviour i s po ten t ia l ly d i ffe ren t fo r d i ffe ren t users(o therwise , use a know ledge-based approach) .

5 .2 .1 Benef i t s /Roles

The genera l benef i t s o f in te r face agents a re th reefo ld . F i rs t ly, they m ake less w ork forthe end user and appl ica t ion deve loper. S econdly, the agent can adapt , ov er t ime , to i t suser ' s p re fe rences and hab it s . F ina l ly, kno w -how am ong the d i ffe ren t users in a

com mu ni ty m ay be shared (e .g . whe n agents l ea rn f rom the i r peers) . Perhaps these w i l lbe unders tood be t te r by d i scuss ing a few of the ro les fo r wh ich M aes an d her team a tM assachuse t ts Ins t i tu te o f Tech nolo gy (MIT) a re bu i ld ing in te rface agents .

Ko zierok and Ma es [25] descr ibe an interface agent , Calen dar Agen t , that ass is ts i tsuser in schedul ing meet ings . I t can lea rn, over t ime , the pre fe rences and com mitm entsof i ts user, e .g . does not l ike to a t tend meet ings on a Fr iday, prefers meet ings in themorn ing . The lea rn ing techniques employed a re memory-based lea rn ing andreinforcement learning. Dent e t a l [26] a lso descr ibe a s imilar learning apprent iceagent , the C alendar Appren t ice (CAP) .

Liebermann [27] descr ibes Le t iz ia , a keyword and heur i s t i c -based search agent ,wh ich ass is t s in W eb browsing . S ince mo s t b rowsers encourage dep th- f i rs t b rowsing ,Le t iz ia conduc ts a b read th- f ir s t search concur ren t ly fo r o ther usefu l loca t ions in w hichthe user may be in te res ted . I t does th i s by 'guess ing ' the user ' s in ten t ion f rom the i rbrowsing behav iour (e.g . keeps re turn ing to som e par t icu la r page) and proceed ing tosearch us ing the search engine . By do ing th i s , i t i s ab le to recommend some o theruseful serendipi tous locat ions .

M aes [23] descr ibes a news f i l tering agent , N ew T, th at helps u sers f i l ter and selecta r ti c les f rom a cont inuous s t ream o f Usene t Netnew s . Ne w T agents a re t ra ined bypresen t ing to them pos i t ive and neg a t ive examples o f wh at shou ld or should no t bere t r ieved . They a re message-conten t and keyword-based bu t a l so explo i t o therin format ion such as the au thor an d source .

5 . 2. 2 I n t e r fa c e A g e n t s - - S o m e C h a l l e n g e s

The key c r it i ci sm o f in ter face agents i s tha t , so fa r, they ten d to func t ion in s tand-a lonefash ions or, a t the mos t , on ly engage in res t r ic ted and task-spec i f ic communica t ionwith ident ical peers [28] . This is not necessar i ly bad but i t would be useful to haveinterface agents being able to negot ia te with their peers as with col laborat ive agents .Furtherm ore, as Mitche l l e t a l note:

" . . .i t r emains to be dem ons t ra ted tha t know ledge lea rned by sys tems l ike CA P canbe used to s ign i f ican t ly reduce the i r users ' wo rk load ." [29]

Som e other chal lenges for interface agents include:


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carrying out exper iments using var ious m achine learning techniques over severaldomains to determine which learning techniques are preferable for what domains

and why ;9 guaran tee ing the users' p r ivacy and the lega l quagm ire which may ensue

fol lowing the f ie lding of such agents [23];

9 extending the range o f appl ications o f interface agents into other areas .

Ho wev er, having s ta ted these, there is no den ying the fact that in terface agents canbe d eploye d in real appl ications in the sho rt term because th ey are s imple , operate inl imited dom ains and require no co-operat ion.

5 .3 M o b i l e A g e n t s - - A n O v e r v i e w


M obi le agents a re sof tware processes capable o f roaming w ide a rea ne tworks (WA Ns)such as the W W W , in te rac ting wi th fore ign hos ts , ga ther ing in format ion on beha l f o fthe i r owners and coming 'back home ' hav ing per formed the du t ies se t them. Thesedut ies m ay range f rom m aking a f l igh t rese rva t ion to ma naging a t e lecom-munica t ionsnetwork. Mobile agents are agents because they are autonomous and they co-operate ,a lbei t d i fferent ly f rom col laborat ive agents . For example, they may co-operate orcommunica te by one agent making the loca t ion of some of i t s in te rna l ob jec t s andme thods kno wn to o ther agen ts .

The k ey hypo thesis underlying mob ile agents is the idea that , in cer ta inappl icat ions , they provide a number of pract ical , though non-funct ional , advantageswhich escape their s ta t ic counterpar ts . For example, as BT's Barry Crabtree notes:" Im agine hav ing to dow nload m any im ages jus t to p ick ou t one . I s i t no t more na tura lto get you r agent to 'go ' to that locat ion, do a local search a nd only t ransfer the chosencom pressed image back across the ne two rk?"

5 .3 .2 H o w M o b i l e A g e n t s W o r k - - A B r i e f Te l e se r ip t Vi e w

Telescr ipt is an interpreted object-or iented and remote programming language whichal lows for the develop m ent of dis tr ibuted appl icat ions [30]. Figure 3 summ arises a par tv iew of the Te lescrip t arch itecture . The Te lescr ipt D eve lopm ent Envi ronment f ID E)comprises , among other things, the engine ( interpreter and run-t ime developmentenvironm ent) , browser, debugge r and associated l ibrar ies .

Telescr ipt appl icat ions consis t of Telescr ipt agents operat ing within a 'world ' or

cyberspace of places and engines; both o f wh ich are objects . The top class inTelescr ipt ' s object hierarchy is the process . A Telescr ipt engine is i t se l f a pre-emptivemu lt i- tasking interpreter which can run m ult iple processes . H ence, the e ngine can hostm ult iple agents that share data/ informa t ion betw een them selves . Furthermore, a placeis a process w hich can c ontain an arbi t rary num ber a nd depth o f other places . Agents ,unl ike places , are objects wh ich canno t contain other processes , but they can 'g o ' f rom


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p l ace t o p l ac e . A n a g en t re q u i ri n g a s e rv i ce de f ined a t som e g iven p l ace m us t go t o t ha tp lace and ca l l the opera t ions the re ( see F ig 3 ). Thus , ' g o ' i s the p r imi t ive w hich a l low s

fo r i n t e r-p roc e s s c om mu n ica t io n . Tw o o r m ore agen t p roce s se s c an m ee t i n a p l ace andm ake u se o f e a c h o t he r ' s s e rv i c e s .

server/operating systemI

Fig . 3 . Par t v iew of Te]escdpt architec ture (adapted f rom W ayn cr [31]) .

A ' g o ' r e q u i r e s a d e s t i n a t i o n p la c e a n d t h e h o s t e n g i n e p a c k a g e s u p th e a g e n t a l o n gw i t h a l l i ts d a t a , s t a c k a n d i n s t r u c t i o n p o i n t e r a n d s h i p s itof f t o t h i s d e s t i n a t i o n p l a c ew h i c h m a y b e a c r os s a v a s t W A N . A t it s d e s t in a t io n , th e o t h e r Te l e s c r ip t - e n a b l e de n g i n e u n p a c k s it , ch e c k s i t s a u t h e n t i c a t io n , s o t h a t it i s t h e n f r e e t o r e s u m e e x e c u t i o na t t h i s n e w p l a c e . W h e n i t fi n is h e s , i t r e t u r n s t o i ts o r i g i n a l h o s t h a v i n g p e r f o r m e d t h et a sk requ i red by i t s owner.

The re a r e o th e r l angu a ges w h ich suppo r t mob i l e agen t sy s t em deve lopmen t ,no t ab ly J ava fr om S un M i c r o sy s t e m s , though it i s no t a r em o te p rog ram m ing l anguage

l ike Te l e sc r ip t . I t is a l s o im p or t a n t t o po in t ou t tha t mob i l e agen t sy s t em s nee d no t on lybe cons t ruc ted u s in g a r e m o t e p r o g ramm ing sy s t em l ike Te l e sc r i p t. W ayn e r [ 31] show sexam ple s o f h o w m ob i l e ag e n t s c an be s c r ip t ed in X l isp . O the r l anguage s t o cons ide ri nc lude Agen t -Te l , S a f e - Te l a n d C /C++ . I ndeed , App leby and S t eward [32 ] p ro to typeda n a w a r d - w i n n i n g C / C + + p r o g r a m m e d m o b i l e a g e n t - b a s e d s y s t e m f o r c o n t r o l l i n gt e le c o m m u n i c a ti o n s n e t w o r k s.

5.3 .3 M obi le Ag ent Ap pl ica tions

Mobi l e agen t app l i c a t i o ns do n o t cu r r en t l y abound bu t a r e l i ke ly t o i nc r ea se i n t hef u t u r e . H o w e v e r, t h e f i r s t c o m m e r c i a l a p p l i c a t i o n w a s S o n y ' s M a g i c L i n k P D A o rpersona l in te l ligen t com m unic a tor [33]. Essen t ia l ly, i t a ss i s t s in m ana ging a us e r ' s e -ma i l , f a c s imi l e , t e l ep h one a n d page r a s we l l a s l i nk ing t he u se r t o Te l e sc r i p t - enab l edm e s s a g i n g a n d c o m m u n i c a t i o n s s e r v i c e s s u c h a s A m e r i c a O n l i n e a n d AT & TPer sonaL ink S e rv i c e s .


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P l u [ 3 4 ] m e n t i o n s t h a t F r a n c e Te l e c o m h a s p r o t o t y p e d s o m e s e r v i c e s b a s e d o nTe lesc r ip t . In one o f the i r dem ons t ra to r s , mob i l e Te lesc r ip t agen t s in t eg ra te r a i lway

t i c k e t i n g a n d c a r r e n t i n g s e r v i c e s . I B M p l a n s t o l a u n c h t h e i r C o m m u n i c a t i o n S y s t e m[ 35 ] w h i c h u s e s m o b i l e a g e n t s f o r p r o v i d i n g a c o m m u n i c a t i o n s s u p e r - s e rv i c e c a p a b l eo f ro u t e in g a n d tr a n s la t i n g c o m m u n i c a t i o n s f r o m o n e s e r v ic e a n d m e d i u m t o a n o t h e r,e .g . m obi l e to desk top , PD A to f acs imi le , speech to tex t .

5.3.4 M o b i l eAgents- - S o m e Challenges

W a y n e r [3 6] l is ts t h e m a j o r c h a l l e n g es o f m o b i l e a g e n t r e s e a rc h a n d d e v e l o p m e n t .

9 Tr a n s p o r ta t i o n - - h o w d o e s an a g e n t p a c k up a n d m o v e f r o m p l a c e t o p l a c e ?

9 Authen t i ca t ion - - how do you ensure the agen t is w ho i t s ays it i s, and isrep resen t ing wh o i t c l a im s to be r ep resen t ing?

9 S e c r e c y - - h o w d o y o u e n s ur e t h a t y o u r a g e n ts m a i n t a i n y o u r p r i v a c y ? H o w d oensure o the r s do no t r ead your pe r sona l agen t and execu te i t fo r the i r own ga ins?H o w d o y o u e n s u r e y o u r a g e n t is n o t k i l le d a n d i ts c on t e n ts ' c o r e - d u m p e d ' ?

9 S e c u r it y - - h o w d o y o u p ro t e c t a g a i n s t v i r us e s ? H o w d o y o u p r e v e n t a n i n c o m i n ga g e n t f r o m e n t e r in g a n e n d l e ss l o o p a n d c o n s u m i n g a ll t h e C P U c y c l e s ?

9 C a s h - - h o w w i l l t h e a g e n t p a y fo r s e r v ic e s ? H o w d o y o u e n s u re t h a t i t d o e s n o trun am ok and run up an ou t rageous b i l l on your behalf? .

In add i tion to these a re the fo l lowing .

9 P e r f o r m a n c e i ss u e s - - w h a t w o u l d b e t h e e f f e c t o f h a v i n g h u n d re d s , t h o u s a n d s o rm i l l io n s o f su c h a g e n t s o n a WA N ?

9 I n t e r o p e r a b i l it y / c o m m u n i c a t i o n / b r o k e r i n g s e r v ic e s - - h o w d o y o u p r o v i d ebroker ing /d i rec to ry type se rv ices fo r loca t ing eng ines and /o r spec i f i c se rv ices?

H o w d o y o u e x e c u t e a n a g e n t w r i t t e n i n o n e a g e n t l a n g u a g e o n a n a g e n t e n g i n ewr i t t en in ano the r l anguag e? Ho w do you pub l i sh o r subsc r ibe to se rv ices , o rs u p p o r t b ro a d c a s t i n g n e c e s s a r y f o r s o m e o t h er c o - o r d in a t i o n a p p r o a c h e s ?

H a v i n g l i st e d s o m e o f t he c h a l l e n g e s o f m o b i l e a g e n t r e s e a rc h , i t m u s t b e n o t e d t h a ts o m e o f t h e m a r e a l r e a d y b e i n g a d d r e s s e d s u c c e s s fu l l y in d e v e l o p m e n t e n v i r o n m e n t sl ike TD E us ing va r iou s t echn iques inc lud ing the fo l lowing :

9 u s i n g A S C I I - e n c o d e d , S a f e - Te l s c r ip t s o r M I M E - c o m p a t i b l e e - m a i l m e s s a g e s f o rt r anspor ta t ion ;

9 us ing pub l i c - and p r iva te -key d ig i ta l s igna tu re t echn o logy fo r au then t ica t ion , cashand sec recy ;

9 p r o v i d i n g li m i t e d l a n g u a g e s t h a t w i l l n o t a l l o w a n a g e n t t o w r i t e t o m e m o r y, s a y,for secur i ty.


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A s a r e su l t, m u c h s o f t w a r e a n d h a r d w a r e w h i c h e x p l o i t m o b i l e a g e n t - b a s e dse rv ices a re cu r ren t ly in the p ipe l ine .

5 .4 I n f o r m a t i o n / I n t e r n e t A g e n t s - - A n O v e r v i e w


I n f o rm a t i o n a g e n t s p e r f o r m t h e r o l e o f m a n a g i n g , m a n i p u l a t i n g o r c o l l a t i n gi n f o rm a t i o n f r o m m a n y d i s tr i bu t e d s o u r c e s . T h e m o t i v a t i o n f o r d e v e l o p i n g i n f o r m a t i o nagen t s i s a t l eas t twofo ld . F i r s t ly, t he re i s s imply a need fo r too l s to manage thei n fo r m a ti o n e x p lo s io n o f t he W W W . E v e r y o n e o n th e W W W w o u l d b e n e f it f r o m t h e m

in the sam e wa y as they a re benef i t ing f rom sea rch fac i l it a to r s such as sp ide r s , l ycos o rwebcrawle r s . Second ly, the re a re vas t f inanc ia l benef i t s to be ga ined . Reca l l t ha tN e t s c a p e C o r p o r a t i o n g r e w f r o m r e l a t iv e o b s c u r i t y t o a b il l io n d o l l a r c o m p a n y a l m o s tovern igh t and a Ne t scap e o r Mosa ic c l i en t s imply o ffe r s genera l b row s ing capab i l i t ie s ,a lbe i t w i th a f ew add-ons . W hoe ver bu i lds the fi r s t u sab le Ne t sc ape eq u iva len t o f ap r o a c t i v e , d y n a m i c , a d a p t i v e a n d c o - o p e r a t i v e W W W i n f o r m a t i o n m a n a g e r i s c e r t a i nt o r e a p e n o r m o u s f m a n c i a l r e w a r d s.

5 .4 .2 H o w I n f o r m a t i o n A g e n t s W o r k

In te rne t agen t s cou ld be mobi l e ; however, t h i s i s no t the norm as ye t . Typ ica l s t a t i co n e s a r e e m b e d d e d w i t h i n a n I n t e r n e t b r o w s e r a n d u s e a h o s t o f I n t e rn e t m a n a g e m e n ttoo l s such as sp ide rs and sea rch e ng ines in o rde r to ga the r the in fo rmat ion . E tz ion i andWeld [37] desc r ibe a s t a t e -o f - the -a r t In temet agen t ca l l ed the In te rne t ' so f tbo t '( s o f t w a r e r o b o t ) . I t a l l o w s a u s e r t o m a k e a h i g h - l e v e l m e n u - b a s e d r e q u e s t s u c h a s' s e n d th e b u d g et m e m o s t o M i t ch e ll a t C M U ' a n d ' G e t a l l o f G i n s b e rg ' s t ec h n ic a lrepor t s tha t a ren ' t s to red loca l ly ' , and the so f tbo t i s ab le to use sea rch and in fe renceknow ledge to de te rmine h ow to sa t i s fy the r eques t in the In te rne t. In do ing so , it is ab leto to le ra te ambigu i ty, omiss ion s and the inev i t ab le e r ro r s in the use r ' s r eques t . E tz ion iand Weld use a s t rong ana logy to a r ea l robo t in o rde r to desc r ibe the i r so f tbo t ' sin t e r face to the In te rne t . For example , they desc r ibe the so t tbo t ' s e ffec to r s to inc ludefrO, t e lne t, m a il and num erous f i l e m an ipu la t ion com m and s inc lud ing m v o r co m pre ss .The sensors p rov ide the so f tbo t wi th in fo rmat ion abou t the ex te rna l wor ld and theyinclude In ternet fac i l i t ies such as arehie , g op he r and nel iind .

5 .4 .3 A B r i e f C r i t ic a l R e v i e w o f I n f o r m a t i o n A g e n t s W o r k

I n f o r m a t i o n a g en t s a r e e x p e c t e d t o b e a m a j o r g r o w t h a r e a i n t h e n e x t c o u p l e o f y e a r s .

A t B T L a b o r a t o r ie s , D a v i e s a n d W e e k s [ 38 ] h a v e d e s ig n e d a n d i m p l e m e n t e d th e J a s p e ra g e n t. J a s p e r a g e n ts w o r k o n b e h a l f o f a u s e r o r a c o m m u n i t y o f u s e rs , a n d a r e a b l e t os to re , r e t ri eve , summ ar i se and in fo rm o the r agen t s o f in fo rmat ion use fu l to the m foundon the W W W . As a use r work s wi th the i r Ja spe r agen t , a p ro f i l e o f the i r in t e res ts i sbu i l t dynamica l ly, based on keywords . A Jaspe r agen t i s ab le to sugges t in t e res t ingW W W p a g e s t o a u s e r b y m a t c h i n g t h e i r p r o fi l e w i th t h o s e o f o t h e r u se r s i n t h e


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comm uni ty. A succes s fu l ma tch re su lt s i n the u se r be ing t o ld o f o the r W W W pages t ha tpeers f ind ' in te res t ing ' .

The k ey prob lem wi th s tat ic in form at ion agents i s in keep ing the i r indexes up- to-da te in the very dyn am ic W W W envi ronment . For th i s reason , and for s imi la r reasonsme nt ion ed in sec t ion 5 .3 , i t i s p robable tha t the m ajor i ty o f fu ture in format ion agentswi l l be o f the mobi le var ie ty. They wi l l be ab le to nav iga te the W W W and s to re i tstop olo gy in a database, say, a t their ho m e s i te .

As regards the c r i ti c i sms and cha l lenges o f in format ion agents , they a re essen t ia l lys imi la r to those of e i ther in te r face or mobi le agen ts , depending on whether theinformat ion agent i s s ta ti c o r m obi le respec t ive ly.

5 .5 R e a c t i v e S o f t w a r e A g e n t s - - A n O v e r v i e w

React ive agents represen t a spec ia l ca tegory o f agen ts wh ich do n o t possess in te rna l ,sym bol ic mod els o f the i r env i ronm ents ; ins tead they respond in a s t imulus- responsem anne r to the presen t s ta te o f the envi ronm ent in wh ich they a re embedded . R eac t iveagents work da tes to research such as Brooks [12] and Agre and Chapman [13] , bu tm any theor ies , a rch i tectures and languages for these sor ts o f agen ts have subsequen t lybeen deve loped .

M aes [39] h igh l igh ts the th ree key ideas which und erp in reac tive agents . F i r s tly,' emerg ent fun c t iona l i ty ' - - reac tive agents a re re la t ive ly s imple and they in te rac t w i tho ther agen ts in bas ic ways . Never the less , complex pa t te rns o f behaviour em erge f romthese in te rac tions w hen the ensemb le of agen ts i s v iewed g loba l ly. Hence , there i s no apriori spec i f ica t ion (or p lan) o f the beh aviour o f the se t-up o f reac t ive agents .Secondly, ' t a sk decompos i t ion ' - - a reac t ive agent i s v iewed as a co l lec t ion ofm odu les which opera te au to nom ous ly and a re respons ib le fo r spec i f ic t asks (e .g .sens ing , motor con t ro l , compu ta t ions , e tc ) . Co m-m unica t ion be tween the m odu les i sm in im ised and o f qui te a low- leve l na ture . No g loba l m odel ex i s t s wi th in any o f theagents and , hence , the g loba l behaviou r has to em erge . Thi rd ly, reac tive agents t end toopera te on represen ta t ions which a re c lose to raw sensor da ta, in con t ras t to the h igh-leve l symb ol ic represen ta t ions tha t abound in the o ther types of agen ts d i scussed so fa r.


The essen t ia l hypothes i s o f reac t ive agent -based sys tems i s a spec i f ica tion of thephys ica l -ground ing hypothes i s , no t to be confused wi th the phys ica l - sym bol sys temhypothes i s . The la t t e r hypothes i s ho lds tha t fo r a phys ica l sys tem to demons t ra tein te l l igen t ac t ion i t should be a phys ica l - symbol sys tem. The phys ica l -groundinghypo thes i s cha l lenges th is long-he ld AI v iew, a rgu ing i t i s f l awed fundam enta l ly, andtha t i t impo ses severe l imi ta t ions on sym bol ic AI-based sys tems . This new h ypo thes i ss ta tes that in order to bui ld a system that is in te l l igent , i t i s necessary to haverepresen ta tions grou nded in the phy s ica l wor ld . Broo ks [40] a rgues tha t th is hypo thes i sobvia tes the need for symb ol ic represen ta t ions o r models because the w or ld becom esi ts ow n bes t mod el . Fur thermore , th is m odel i s a lway s kep t up to da te s ince the s ys temis connec ted to the wor ld v ia sensors and /or ac tua tors . Hence , the reac t ive agents


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hypothes i s may be s ta ted as fo l lows : "Smar t agen t sys tems can be deve loped f romsimple agen t s which do no t have in te rna l symbol ic mode ls , and whose ' smar tness '

de r ives f rom the em ergen t behav iour o f the in te rac t ions o f the va r ious agen t s . "The ke y benef i t s wh ich mot iva te reac t ive agen t s work i s the hope tha t they w ould

be m ore robu s t and fau l t to le ran t than o ther agen t -based sys tems , e .g . a s ing le a gen t inan ensemble may be los t wi thou t any ca tas t roph ic e ffec t s . Other benef i t s inc ludef lexibi l i ty and adaptabi l i ty in contras t to the inf lexibi l i ty, s low res pons e t imes andbr i t t leness o f c lass ical AI sys tem s.

5.5.2 Rea ct ive Ag ent Ap pl icat ions

There a re re la t ive ly few reac t ive so f tware agen t -based app l ica t ions. Par t ly, due to th i sreason , the re i s no s tandard m ode to the i r opera t ion ; r a the r, they t end to d epend on thereac t ive agen t a rch i t ec tu re chosen .

Perhaps the mos t ce lebra ted reac t ive agen t a rch i t ec tu re i s Brooks ' subsumpt ionarch i tec tu re [41]. The a rch i tec tu re cons i s ts o f a se t o f modules , each o f wh ich i sdesc r ibed in a subsumpt ion l anguage based on augmented f in i t e s t a te mach ines(AFSM). A n A FS M is t r iggered in to ac t ion i f i ts inpu t signa l exceed s some th resho ld ,thou gh this i s a lso dep ende nt on the values o f suppre ss ion an d inhibi t ion s ignals in tothe AFSM . N ote tha t AFS M s represen t the on ly p rocess ing un i t s in the a rch i t ec tu re , i. e.the re a re no sym bols as in c lass ical AI work . T he mod ules a re g rouped and p laced inlayers which work asynchronous ly, such tha t modules in a h igher l eve l can inh ib i tthose in lowe r l ayers ( see F ig 4 ) . Each l ayer has a ha rd-wi red purpo se o r behav iour, e .g .to avo id obs tac les o r to enab le /con t ro l wander ing . Th is a rch i t ec tu re has be en u sed tocons t ruc t a t l eas t t en m obi le robo ts a t M IT.

= =

2 - I e x p , o r e I -- I I rw a n d e r

r I a v o i d o b s t a c l e s II v

==

Fig. 4. Brooks' subsumption architecture.

Arguab ly, the m os t bas ic reac t ive a rch i t ec tu re is tha t based on s i tua ted-ac t ion ru les .A s i tuated act ion agen t ac ts essent ia l ly in w ays w hich are 'a ppr opr ia te ' to i ts s ituat ion,where ' s i tua t ion ' r e fe r s to a po ten t i a lly com plex com bina t ion o f in te rna l and ex te rna leven t s and sta tes [42] . S i tua ted-ac t ion ' agen t s ' have been used in PEN GI , a v ide o gam e[13] , and to s imulate ant socie t ies wh ere ea ch ant i s m ode l led as an agent , and a l imi ted

ecosys tem com pose d o fb io tap es , shoa ls o f f ish and f i shermen [14] .

5 .5 .3 A Br ie f Cr i t ica l Rev iew of Reac t ive Agents W ork

M any c r i ti c i sms can be l eve l l ed aga ins t r eac t ive so f twa re agen t s and the i rarchi tectures . Fi rs t ly, as a l ready noted, there are too few appl icat ions about based on


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them. Secon dly, the scop e o f the i r app licab i li ty is cur ren t ly l imi ted , main ly gam es ands imula tions . To b e fa i r, i t i s st il l ea r ly days fo r such resea rch - - a rguab ly, sym bol ic AI

d id no t s t a rt de l ive r ing an y use fu l indus t r ia l app l ica t ions un t i l mo re than two decadesaf te r it was born . S o , the re i s a c lea r need to exp and the rang e o f languages , theor ies ,a rch i t ec tu res and app l ica t ions fo r reac t ive agen t -based sys tems . Th i rd ly, i t i s no to b v i o u s h o w t o d e s ig n s u c h s y s te m s s o th a t y o u r i n te n d e d b e h a v i o u r e m e rg e s f r o m t h ese t -up o f agen t s. Ho w m any of such agen t s a re requ i red fo r some app l ica tions?Cur ren t ly, s ince i t i s no t a l lowable to te l l the agen t s how to ac h ieve so m e goa l , as wi thgene t ic a lgor ithms:

"O ne has to f ind a ' dyn am ics ' , ... i nvo lv ing the sy s tem and the env i ron m ent whichwi l l converge toward s the des i red goa l . " [43]

This would no t on ly be t ime-consum ing , bu t i t a l so sm acks o f ' tr i a l and e r ro r ' wi tha l l it s a tt endan t p rob lem s . Fur the rm ore , Maes [43] po in ts o u t tha t th i s s i tua ted agen t sw ork has some imp or tan t l imi ta t ions p rec i se ly because " o f the i r l ack o f exp l ic it goa l sand goa l -hand l ing capab i l i ti e s" , r equ i r ing the des igners o f the sy s tems to p reco m pi le o rhard-wi re the ac t ion se lec t ions . Hence , whi le a p lann ing approa ch l eaves muc h to theagen t , the s i tua ted agen t s approach l eaves much to the des igners . Four th ly, how a res u c h s y s te m s e x t e n d e d , s c a le d u p o r d e b u g g e d ? W h a t h a p p e n s i f t h e ' e n v i r o n m e n t ' i sc h a n g e d ?

5 .6 H y b r i d A g e n t s - - A n O v e r v i e w


So fa r, f ive types o f agen t s have bee n rev iew ed - - co l l abora t ive , in te rface , mo bi le ,In te rne t and reac t ive agen ts . The d eba tes as to which o f them i s ' be t t e r ' a re academic ,s te r i l e and ra the r p remature . S ince each type has o r p romises i t s own s t reng ths anddef ic ienc ies , the t r i ck as a lways i s to maximise the s t reng ths and min imise thed e f i ci e n c ie s o f t h e m o s t r e l e v a n t t e c h n i q u e f o r y o u r p a r t ic u l a r p u r p o s e . F r e q u e n t l y, o n ew ay of do ing th i s is to ado p t a hyb r id approach , l ike Ma es [43], wh ich b rou ght toge thersom e o f the s t reng ths o f bo th the de l ibe ra t ive and reac t ive pa rad igms . In such a case ther e a c ti v e c o m p o n e n t , w h i c h w o u l d t a k e p r e c e d e n c e o v e r t h e d e l ib e r a ti v e o n e , b ri n g sabout the fo l low ing benef i t s - - robus tness , f a s te r r esponse t imes and adap tab i li ty. Thede l ibe ra t ive pa r t o f the age n t wou ld hand le the lon ger t e rm go a l -o r ien ted i ssues. H ence ,h y b r i d a g e n t s r e f e r t o t h o s e w h o s e c o n s t i tu t io n i s a c o m b i n a t io n o f t w o o r m o r e a g e n tph i losoph ies w i th in a s ingu la r agen t.

5 .6 .2 H y b r i d A g e n t A r c h i t e c t u r e s

As i s the case wi th reac t ive agen ts , the re a re few hyb r id agen t a rch i t ec tu res. Typ ica l ly,how ever, they have a l ayere d a rch i t ec tu re as is ev iden ced by In teRR aP [44] andTo u r i n g M a c h i n e s [ 45 ]. B o t h a r e d e s c r ib e d b r i e f l y b e l o w.

M ul le r et a l 's In teRR aP a rch i t ec tu re (F ig 5 ) com pr i ses th ree con t ro l l ayers - - thebehav iour-based l ayer (BBL) , the loca l p lann ing l ayer (LPL) and the co-opera t ive


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planning layer (CPL ) . The reac tive par t o f the f ram ewo rk is implem ented by the B BLwh ich conta ins a se t o f s i tua t ion-ac t ion ru les . These descr ibe the a gen t ' s reac t ive sk i l l s

which implement fas t s i tuat ion recogni t ion in order to react to t ime-cr i t ical s i tuat ions .The in te rmedia te LPL implements loca l goa l -d i rec ted behaviour whi le the topmostCP L enables the ag ent to p lan /co-opera te wi th o ther agen ts in o rder to ach ieve m ul t i -agent p lans , as wel l as reso lve conf l ic ts . LPL and C PL a l low for more de l ibera t ion . Thethree layers a ll work asyn chrono us ly wi th d i ffe ren t mo dels in the agen t ' s kn ow ledgebase - - BBL, LPL and CPL ope ra t e w i th t he wor ld , men ta l and soc i a l mode l srespec tive ly. The In teR RaP arch i tec ture has been eva lua ted by cons t ruc t ing a FO R K Sappl ica tion which s imu la tes fo rk l i f t robots w ork ing in an au tom ated load ing dockenvi ronment .

agent co ntrol unit~__ ~ co-opera tiveplanning ayer

~ ~ lo c a lplanning aye r

agent KBsocialmodel

mentalmodel ~ .....

worldmodel

t ' - II perception ]

tFig. 5.

communication action

t *

behaviour-based ayer

I w orld interface/body

The InteRRaP hyb rid architecture (adopted from Fischer et al [46]).

Ferguson ' s Tour ingMachines [45] a rch i tec ture , which i s s imi la r to Brooks 'subsum pt ion a rch itec ture ( see F ig 4) , cons i s ts o f th ree cont ro l l ayers - - the reac t ive

layer, the p lanning layer and the mod el l ing layer, wh ich a l l work concur ren t ly. A keydis t inc t ion be tween Tour ingMachines and Brooks ' subsumpt ion a rch i tec ture on theone hand , and In teRRaP on the o ther i s tha t the former a re hor izonta l a rch i tec tureswhile the la t ter is a ver t ical archi tecture . This means that a l l the layers inTou r ingM achines and the subsum pt ion a rch i tecture have access to the percep t ion da taand can cont r ibu te to the ac t ions (as show n in F ig 4), wh i le on ly the bo t tom layer inInteR RaP receives and acts on the perceptu al data (see Fig 5). Therefore , to achiev e co-ord ina t ion in Tour ingM achines , Ferguson has cont ro l ru les capable o f suppressing theinput to a ce r ta in layer, s imi la r to the inh ib i t ion mechanisms in the subsumpt ion

archi tecture .

5.6.3 A Brie f Cr i t ica l Review of Hybr id Arch i tec tu res and Cha l lenges

Hy br id agent a rch i tec tures a re s t il l r e la t ive ly few in num ber bu t the case for hav in gthem i s overwhelm ing . There a re usua l ly th ree typ ica l c r it i ci sms o f hybr id a rch i tec tures


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in general . Firs t ly, hybridism usual ly t ransla tes toad hoc or unprincipled designs.Secondly, ma ny hyb rid archi tectures tend to be very appl icat ion-specific , and for good

reasons too. Thirdly, the theory which underpins hybrid systems is not usual lyspecified. Therefore , the chal lenges for hybrid age nts research wo uld appear to be qui tes imilar to those ident i f ied for react ive agents . In addi t ion to these, hybrids of agentphi losophies other than react ive/del iberat ive ones wo uld be expected to appear.

5 .7 H e t e r o g e n e o u s A g e n t S y s te m s - - A n O v e r v ie w

5 .7 .1 H y p o t h e s i s, M o t i v a t i o n a n d B e n e f i ts

Heterogeneous agent systems refer to an integrated set-up of a t least two or moreagents which be long to two or m ore d i ffe ren t agen t c lasses . A he te rogeneous agentsystem m ay also contain hybrid agents . Gen esereth and K etchpel [47] ar t iculate c lear lythe motivat ion for heterogeneous agent systems. The essent ia l argument is that thewo rld abounds with a r ich dive rs i ty of sof tware products. Th ough these programs w orkin isolat ion, there is an increasing dem and to have them interoperate , hopefu l ly in sucha manner tha t they prov ide grea te r ' added-va lue ' as an ensemble than they doindividual ly. Indeed, a new dom ain cal led agent-based sof tware engineer ing has be eninvented in order to faci l ita te the interoperat ion of m iscel laneous so f tware agents .

A key requirement for interoperat ion among hetero-geneous agents is having anagent comm unica t ion language th rough w hich the d i ffe ren t sof tware ' agen ts ' cancommunicate with each other. Genesereth and Ketchpel [47] note that agent-basedsoftware engineer ing is of ten compared to object-or iented programming in that anagent, l ike an object, provides a message-based interface to i ts internal data structuresand algori thms. How ever, they note that there is a key dis t inction - - in object-or ientedprogramming , the m eaning of a m essage m ay d i ffe r f rom objec t to ob ject , whereas inagent-based sof tware engineer ing, agents use a common language with agent-independent semant ics . They begin to address the par t iculars of such an agent-independent communica t ion language th rough ACL, an agent communica t ionlanguage they have been deve lop ing .

5 .7 .2 H o w H e t e r o g e n e o u s A g e n t S y s t em s W o r k

To comm ence , the ra ther spec if ic def in i t ion i s p rov ided of the w ord ' ag en t ' p roffe redin agent-based sof tware engineer ing. I t def ines a sof tw are agent as such ' i f and only i fi t comm unica tes cor rec tly in an agent com mu nica t ion language ' [47]. I f new agents a reconstructed such that they abide by this dic tum, then put t ing them together in aheterogeneous set-up is possible , though not t r ivia l . However, wi th legacy sof tware,they need to be converted into sof twa re agents f i rs t . Genesereth and K etchpel [47] no tethat there are three wa ys of doing this convers ion. Firs t ly, the legacy sof tware m ay berewr i tt en to ta l ly - - a mo s t cos t ly approach . Secondly, a transducer approach m ay beused. The t ransducer is a separate piece of sof tware which acts as an interpreterbe tween the agent communica t ion language and the legacy sof tware ' s na t ivecom m unicat ions protocol . This is the fa voure d approach in s i tuat ions wh ere the lega cy


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code m ay be too de l i ca te to t amper wi th o r i s unava ilab le . Las t ly, the re i s the wrapp ertechn ique in which som e code i s 'i n jec ted ' in to the l egacy p rogram in o rder to a l low i t

c o m m u n i c a t e i n A C L . T h e w r a p p e r c a n a c c e s s d i r ec t l y a n d m o d i f y th e p r o g r a m ' s d a t as t ruc tu res . Th i s i s c lea r ly a more in te rven tion i s t approach , bu t o ffe r s g rea te r e f f i c iencythan the t r ansduc t ion approach .

Onc e the agen t s a re ava il able , the re a re two poss ib le a rch i t ec tu res to choose f rom- - one in which a l l the agen ts hand le the i r ow n co-o rd ina t ion o r ano ther in wh ichgroups o f agen ts can re ly on spec ial sys tem programs to ach ieve co-ord ina t ion . Thed isadvan tage o f the fo rm er is tha t the com mu nica t ion ov erhead do es no t ensuresca lab i li ty. As a consequ ence , the federa ted ap proach ( see F ig 6 ) i s typ ica l ly p re fe r red .

I~176 I e n '1 I a g ~ I I ag~17I a0e~f a c i l i t a t o r f a c i l i t a t o rt f

Fig. 6. A federated system (adapted from Genesereth and Ketchpel [47]).

In the fede ra ted se t-up o f F ig 6 , the re a re f ive agen t s d i s t ribu ted on tw o m achines .T h e a g e n t s d o n o t c o m m u n i c a t e d i r e c t l y w i t h o n e a n o t h e r b u t d o s o t h r o u g hinterm ediar ies cal led faci l ita tors . Essent ia l ly, the agen ts surre nder som e o f thei rau ton om y to the fac i l i ta to r s wh o a re ab le to loca te o the r agen t s o n the ne tw ork capab leof p rov id ing var ious se rv ices . The fac i l it a to rs a l so es tab l i sh the conne c t ions ac ross thee n v i r o n m e n t s a n d en s u r e c o r r e ct ' c o n v e r s a t i o n ' a m o n g a g e nt s. A R C H O N [ 22 ] a n dPA C T [48] used such an a rch it ec tu re .

The work on he te rogeneous agen t sys tems i s ongo ing and the re i s a need fo rmethodolog ies , too l s , t echn iques and s tandards fo r ach iev ing such in te roperab i l i tyam ong he te rog eneou s in format ion sources .

6 . S o m e G e n e r a l I s s u e s

A n o v e r v i e w o f a b r o a d ra n g e o f w o r k w h i c h g o e s u n d e r t h e b a n n e r o f ' a g e n t s ' h a sbeen p rov ided , toge ther wi th the i r va r ious p romises as we l l a s the i r cha l l enges . Webe l ieve , l ike G re i f [49] , tha t agen ts can have an e norm ous e ffec t , bu t tha t th i s wi l lappea r in every day p roduc t s as an evo lu t ionary p rocess . Gr e i f no tes , co r rec t ly in our

v iew, tha t agen t s would in i t i a l ly l everage s imple r t echno log ies ava i l ab le in mos tapp l ica t ions (e .g . word p rocessors , sp readshee t s o r knowledge-based sys tems) . Thenthey would g radua l ly be evo lved in to more compl ica ted app l ica t ions , do ing fo re x a m p l e , r e a l w o r k - f l o w m a n a g e m e n t o r c o n t r o l l i n g r e a l t e l e c o m m u n i c a t i o n sn e t w o r k s .


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Ho we ver, apar t f rom the techn ica l i s sues con s idered so fa r, the re a re a l so a range o fsoc ia l and e th ica l p rob lem s tha t a re loom ing , fo l lowing the l a rge-sca le f i e ld ing o f agen t

techno logy, wh ich soc ie ty wo uld have to g rapp le wi th th rou gh var ious l eg is la tion .9 P r i v a cy - - h o w d o y o u e n su r e y o u r a g e n ts m a i n ta i n y o u r p r i v a c y w h e n a c t in g o n

y ou r behalf?.

9 Respon s ib i li ty which goes wi th re l inqu i shed au thor i ty - - w hen yo u re l inqu i shsom e o f yo ur respons ib i l ity to so f tware agen t s , be aw are o f the au thor i ty tha t i sb e i n g tr a n s f e rr e d t o th e m . H o w w o u l d y o u l i k e t o c o m e h o m e a f t e r a l o n g h a r d d a yt o f in d y o u a r e t h e p r o u d o w n e r o f a u s e d c a r n e g o t i a te d f o r a n d b o u g h t , c o u r t e s yo f o n e o f y o u r s o f tw a r e a g e n t s?

9 L e g a l i ss u es - - i m a g i n e y o u r a g e nt , w h i c h y o u b o u g h t o f f - t h e - s h e l f a n dcus tomised , o ffe r s som e bad adv ice to o ther pe er agen t s , r e su l ting in l i ab il it ie s too t h e r p e o p l e - - w h o i s r e s p o n si b le ? T h e c o m p a n y w h o w r o t e t h e a g e n t ? Yo u w h ocus tom ised it ? Bo th?

9 E th ica l i s sues - - a l ready, E ichm ann [50] and E tz ion i and W eld [37] a re con cern edenou gh ab out the e th ics o f so f tware agen t s tha t they hav e p rop osed e t ique t t es fo ri n f o rm a t i o n s e rv i c e a n d u s e r a g e n ts a s t h e y g a t h e r i n f o r m a t i o n o n t h e W W W .

However, such i s sues a re no t tha t c r i t i ca l immedia te ly, bu t wi l l become so in them e d i u m t o l o n g te r m .

7. Conclusions

"Sm ar t agen t s a re he re to s tay. Once un leashed , t echno log ies do n o t d i sappear. " [51 ]Th is paper has p i l fe red f rom a d iverse l i t e ra tu re in o rder to overv iew the rap id ly

e v o l v i n g a re a o f s o f t w a r e a g en t s. O n l y W o o l d r i d g e a n d J e n n i n g s [6 ] h a v e a t t em p t e d a

s imi la r ex tens ive rev iew o f th i s a rea , which they do f ro m a theor ies , a rch i t ec tu re andl a ng u a g e s a n g l e. T h i s p a p e r h a s o v e r v i e w e d t h e s a m e a r e a f r o m t h e v i e w p o i n t o f t h ec lea r d ive rs i ty o f agen t s be ing resea rched in un ivers i t i e s and resea rch l abora to r ieswor ld -wide . I t s a im has been to p rov ide a use fu l con t r ibu t ion to unders tand ing th i sexc i t ing f i e ld o f so f tw are agen ts . A m ore de ta i l ed expos i t ion o f th is top ic is ava i l ab le inN w a n a [ 5 2 ].

Acknowledgements

T h e m a n y i n f o r m a l d i s c u s s i o n s w i t h B a r r y C r a b t r e e , M a r k Wi e g a n d , P a u l O ' B r i e n ,R o b i n S m i th a n d N a d e r A z a r m i , w h i c h h a v e s h a p e d s o m e o f t h e v i e w s p r o p o u n d e d i nt h is p a p e r, a re g r a t e f u l ly a c k n o w l e d g e d . T h i s w o r k w a s f u n d e d b y B T L a b o r a t o r i es .


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a brief introduction to software agent technology

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