A Case-Based Reasoning Approach for Norm Adaptation

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1 A Case-Based Reasonng Approach for Norm Adaptaton Jord Campos 1, Mate López-Sánchez 1,andMarcEsteva 2 1 Unverstat de Barcelona, 585 Gran Va, Barcelona, Span {jcampos,mate}@maa.ub.es 2 IIIA - CSIC, Campus UAB, Bellaterra, Span marc@a.csc.es Abstract. Exstng organsatonal centred mult-agent systems regulate agents actvtes. However, populaton/envronmental changes may lead to a poor fulflment of system s goals, and therefore, adaptng the whole organsaton becomes key. In ths paper, we propose to use Case-Based Reasonng learnng to adapt norms that regulate agents behavour. Moreover, we emprcally evaluate ths approach n a P2P scenaro. 1 Introducton Developng Mult Agent Systems (MAS) s a complex task due to the dffcultes of havng flexble and complex nteractons among autonomous enttes. Organsng such systems to regulate agent nteractons helps to predct/regulate the system s evoluton wthn certan bounds. However, certan envronmental or populaton changes may decrease ts ablty to acheve ts organsatonal goals. Thus, adaptng such an organsaton s now becomng an mportant topc [10]. Concernng ths adaptaton, we propose to add an Assstance layer [6] n charge of t, nstead of expectng agents to ncrease ther behavour complexty t s relevant n open MAS, snce there s no control over agent code. In partcular, we use ths layer to adapt norms that are part of system s organsaton. Notce, that these norms regulate agents actvty by boundng ther actons, but agents stll keep ts degree of freedom to choose ther actual actons. Thus, ths addtonal layer can adapt the organsaton whle preservng agents autonomy. However, the relatonshp between these norms and system s outcomes makes the adaptaton process very complex, snce there s no drect mappng among them. Such a process can be coded by the system desgner or learnt. Though, due to dffcultes to defne an optmal mechansm, we advocate by the learnng approach. In ths paper, we use a Case-Based Reasonng method, whch faces new stuatons based on past experence [2]. As an llustraton, we present a Peer-to-Peer sharng network (P2P) scenaro where computers contact among them to share some data. In such a scenaro, the relatonshp among computers actvty, the network traffc and the tme requred to share a datum s complex. Next secton 2 provdes detals on related prevous work. Afterwards, our proposal s descrbed n sectons 3-4 and evaluated n secton 5. Fnally, our conclusons are presented n secton 6. E.S. Corchado Rodrguez et al. (Eds.): HAIS 2010, Part II, LNAI 6077, pp , c Sprnger-Verlag Berln Hedelberg 2010

2 2 Related Work A Case-Based Reasonng Approach for Norm Adaptaton 169 Wthn MAS area, organsaton-centred approaches regulate open systems by means of persstent organsatons e.g. EI [11]. Even more, several of these approaches offer mechansms to update ther organsatonal structures at run-tme e.g. Mose+ [4]. However, most work on adaptaton maps organsatonal goals to tasks and look for agents wth capabltes to perform them e.g. OMACS [10]. Consequently, these approaches cannot deal wth scenaros that lack of ths goal/task mappng, lke our case study. In order to deal wth ths sort of scenaros, our approach uses norms to nfluence agent behavour, nstead of delegatng tasks. Specfcally, our approach uses a norm adaptaton mechansm based on socal power. In ths sense, there are other works that also use the leadershp of certan agents to create/spread norms e.g. the role model based mechansm [9]. Besdes, most works on norm emergence are agent-centred approaches that depend on partcpants mplementaton and they rarely create/update persstent organsatons e.g. nfecton-based model [15]. Relatng norms and overall system behavour s a complex ssue that ncreases ts ntrcacy when there s no control over partcpant s mplementaton. In our approach, ths task s dstrbuted among some empowered agents that fnally reach an agreement about norm updates. Currently, they use a votng scheme to agree on actual norms, but they could use some other agreement mechansms present n the lterature e.g. argumentaton protocols [3]. In partcular, these agents take ther local decsons usng the Case-Based Reasonng (CBR) learnng technque descrbed n [2], whch faces new stuatons based on past experence. The Autonomc EI [5] also use CBR to adapt ther organsaton, takng a centralsed approach. On the contrary, we take a dstrbuted approach both at the processng and knowledge levels as defned n [14]. Regardng our P2P scenaro, there are network management perspectve approaches that try to promote local communcatons but they cannot drectly act on network consumpton to balance net capacty and traffc e.g. P4P [16]. From a MAS angle, there are works where agents adapt local norms usng local nformaton but they cannot reason/act at an organsatonal level e.g. [12]. 3 Assstance n P2P Scenaro Our approach conssts n provdng support to the coordnaton of agents see coordnaton support n [6]. In fact, we proposed a generc Two Level Asssted MAS Archtecture (2-LAMA [8]) to help agents to partcpate n Table 1. Results n P2P scenaro tme cnet h data cml BT L.a L.b

3 170 J. Campos, M. López-Sánchez, and M. Esteva Fg LAMA n P2P scenaro organsatonal-centred MAS. We use ths generc archtecture to develop systems that self-adapt ther organsaton dependng on ther evoluton. In partcular, we model our Peer-to-Peer sharng network case study (P2P) as a MAS wth two level of organsed agents see Fgure 1. Both levels share the same goal, whch s that all partcpant agents obtan the data by consumng mnmum tme. On the one hand, we model the set of computers that share some data as agents (Ag DL = {P 1...P n })wthnadoman-level (DL). Its sngle role peer and the relatonshps among them (.e. the overlay network) conform the socal structure of ther organsaton. Ths organsaton also has ts own socal conventons: a sharng protocol derved from standard BtTorrent [8] and two norms (Norm DL ). Frst norm lmts agents network usage n percentage of ts nomnal bandwdth: normbw DL = a peer cannot use more than max BW bandwdth percentage to share data. Ths way, t prevents peers from massvely usng ther bandwdth to send/receve data to/from all other peers. Notce that a massve network use, may saturate t, and therefore, t may delay all communcatons. Second norm lmts the number of peers to whom a peer can send the data: normf R DL = a peer cannot smultaneously send the data to > max FR peers. On the other hand, n order to support the coordnaton of prevous agents, we add an Assstance layer to the descrbed MAS. Currently, ths support conssts n adaptng doman-level s organsaton to changng crcumstances. More precsely, t conssts n adaptng two DL s organsatonal components: norms see secton 4 and part of the socal structure by suggestng socal relatonshps among pars of DL agents (rel_sugg), see [8]. These adaptatons are performed by a meta-level (ML) set of agents (Ag ML = {A 1...A m }) that play the role assstant. Each assstant s n charge of a dsjont subset of Ag DL (cluster). In fact, assstants use an nterface among both levels to collect local nformaton about connecton bandwdths and communcaton latences (.e. envronment observable propertes, EnvP) and about who has the datum (.e. agent

4 A Case-Based Reasonng Approach for Norm Adaptaton 171 observable propertes, AgP ). In partcular, each assstant counts on both detaled nformaton from ts cluster and aggregated nformaton from other clusters suppled by other assstants. They weght them to combne the nformaton before startng ts own decson process n current tests, each assstant gves the same mportance to ts local nformaton than to remote one. 4 Learnng Norm Adaptaton As we mentoned, n P2P scenaro, adaptng norms to obtan desred system outcomes s a complex task. Manly, because there s no drect mappng between norms and system s behavour. For nstance, when updatng a norm (e.g. ncreasng max FR ), t s dffcult to foresee the effect of organsatonal changes (e.g. how many data messages wll be transmtted), and t s even more complex to antcpate system s outcomes (e.g. the total tme requred to spread data). In order to face ths complexty, the meta-level uses a learnng technque to decde how to adapt doman-level norms dependng on current system status. In partcular, we apply a CBR [2] learnng approach, to suggest norm updates (soluton) to a new system status (problem) based on smlar prevous stuatons (prevous cases). Our CBR approach s based on a heurstc that tres to algn the amount of servng/recevng capacty see [7]. In fact, the heurstc tself s used by our CBR to suggest a soluton when no smlar cases are found. Case descrpton. The descrpton of a problem and ts soluton conforms a case that can be stored as a prevous case n a case base. Theformer(Prob) s descrbed by a set of attrbutes (Attrbs) derved from measures perceved through the nterface as they derve from observable measures, there are not unknown attrbutes. In partcular, we use the followng dscretsed attrbutes to descrbe a problem: srvcap, t ndcates f there s enough servng capacty to serve all recevng peers by comparng the bandwdth of all servng peers wth the bandwdth of all recevng peers (rcvbw); netsat, t estmates the network saturaton by comparng the actual recevng bandwdth of recevng peers and ther expected bandwdth (rcvexpbw = rcvbw max BW,sncemax BW lmts the data njected towards recevng peers); wat, t reflects the amount of peers that lack the datum and are not recevng t currently; srato, t ndcates sources maxmum rato to spread the datum, thus t s derved from current frends norm; bwusg, t ndcates the bandwdth used by peers n ther communcatons, thus t s derved from current bandwdth lmt norm. Besdes, a soluton s descrbed by two dscrete attrbutes: vfr, t ndcates how to update max FR by ncreasng one unt, decreasng one unt, keepng the same value or avodng nfluencng t (.e. a blank ballot-paper); vbw, t defnes how to adapt max BW by settng t to 100%, keepng ts value or dvdng t by two. CBR Cycle. There are four man phases [2]: retreve, reuse, revse and retan. The frst phase (retreve) fetches the most smlar cases (retrcases) fromthe case base (casebase) as llustrated n left sde of Algorthm 1. It starts wth an empty lst of cases and a mnmum reference smlarty (bests) see lne 2. Then, t traverses the case base lne 3 computng the smlarty (σ, see

5 172 J. Campos, M. López-Sánchez, and M. Esteva Algorthm 1. Retreve(left&top-rght) & Reuse(bottom-rght) phases. 01 def retreve( newcase ): 11 f ( retrcases s empty ): 02 retrcases = {} ; bests = 0 12 heucase = Heurstc.solve(newCase) 03 foreach prevcase n casebase: 13 retrcases = { heucase } 04 s = σ ( prevcase.prb, newcase.prb ) 14 return retrcases 05 f ( s > MIN_SIM ): 06 case ( s > bests ): 01 def reuse( retrcases, newcase ): 07 retrcases = { prevcase } 02 f ( δ(retrcases) > MAX_DIV ) 08 bests = s 03 heucase = Heurstc.solve(newCase) 09 case ( s bests ): 04 retrcases = { heucase } 10 retrcases=retrcases {prevcase} 05 sol = adapt( retrcases, newcase ) 06 return Case( newcase.prb, sol ) below) of each prevous case s problem descrpton (prevcase.prb) wth the new problem (newcase.prb) lne 4. In case ths smlarty s greater than a mnmum trusted smlarty (MIN_SIM) the case s a canddate to be retreved lne 5. In partcular, f ths smlarty s greater than any prevous one lne 6 then the prevous case s the one to be retreved lne 7. Alternatvely, f the smlarty s equal to prevous greatest one lne 9 then current prevous case s collected wth the rest of smlar ones lne 10. In other words, t tres to return the most smlar prevous case, although t can return more cases when they have nearly the same smlarty. However, f no prevous case has the mnmum trusted smlarty to consder t s representatve enough to adapt ts soluton to the new problem lne 11, the algorthm executes the heurstc lne 12 to solve ths unknown problem.fnally, n both cases the cases are returned lne 14. The case smlarty functon (σ) among two problems (p x,p y Prob) conssts on computng the attrbute smlarty functon (ς ) among correspondng values of the same attrbute (a px,a py weghted manner: σ(p x,p y )= ( Attrbs w σ ς (a px Attrb ) to aggregate them n a,a py ) ), w σ =1.Inorder to compute ths attrbute smlarty functon (ς ), we defne a label dstance functon (λ ) that provdes a numerc dstance among two dscrete labels (e.g. λ watng (NONE, NONE) =0, λ watng (NONE, FEW) =1, λ watng (NONE, A_LOT) =2). In fact, we regard dscrete labels as an ordered set of equdstant values. Then, we defne ς as an nverse mappng from labels s dstance [0..λ MAX ] to the [0..1] nterval: ς (a px,a py )=1 λ(apx,a py ) λ MAX. In sum, n both smlarty functons (σ, ς ), a 0 means no concdence at all and a 1 means that the tems are equal. From retreved cases, the second CBR phase (reuse) employs ther solutons to buld a new one for the current case. In case there s more than one smlar case, we count on a dvergence functon (δ) to compute the dvergence among them t s the standard devaton of vfr dscrete values converted nto ntegers, snce n our experments vbw was correlated wth t. Thus, reuse phase starts by checkng f the dvergence of retreved cases s greater than a maxmum trusted dvergence (MAX_DIV) see lne 2 n rght sde of Algorthm 1. In such a case, t consders that prevous cases solutons are too contradctory to provde a good sngle soluton. Hence, the heurstc s used lnes 3-4. Once there s a set of slghtly dvergent prevous cases notce that a sngle prevous case has

6 A Case-Based Reasonng Approach for Norm Adaptaton 173 no dvergence t adapts ther soluton to the current problem lne 5. Ths task can take nto account () all retreved solutons but also () the dfferences between the retreved problems and the current one. In current mplementaton, our adapt functon uses only the former (). In partcular, t returns a soluton composed by the most frequent vfr and the most frequent vbw. In case there s a te, the less conservatve actons (.e. change values) have prorty over the more conservatve ones (.e. keep the same values) snce they may make the system evolve n a dfferent way and avod a te n a subsequent adaptaton process. Next, the thrd phase (revse) requres a way to evaluate the soluton, but current performance measure (total tme) s unknown untl the end of executon.e. there s a credt assgnment problem [13]. As we are workng on ths topc n the P2P scenaro, current mplementaton has only the fourth phase (retan). It conssts on storng only the new prevous cases returned by the heurstc. Ths way, the case base grows every tme the heurstc s used when we mplement the thrd phase, the system wll revse ts adapted solutons and retan them f they are representatve enough. After each assstant computes a convenent update for norm parameters usng CBR, all of them agree on ther actual modfcaton usng a votng approach n case there s a te, parameters are not modfed. Fnally, each assstant sends to ts doman-level agents the norms f they have been modfed n current mplementaton peers do not volate norms but they adapt ther behavour when recevng a new norm specfcaton. As applyng norm changes has an assocated cost e.g. cancellng some started data transmssons, the norm adaptaton process s performed at an emprcally tested tme nterval (adapt nterv ) specfed n next secton. 5 Emprcal Evaluaton In order to test our approach, we have mplemented a P2P MAS smulator. Ths smulator s mplemented n Repast Smphony [1] and provdes dfferent facltes to execute tests and analyse results. As t smulates both agents and network components, t allows to execute dfferent sharng methods wth dentcal ntal condtons. Thus, we have performed several tests on BtTorrent and 2-LAMA approaches to emprcally evaluate our proposal s performance. The evaluated approaches n ths work are: a sngle-pece verson of the standard BtTorrent protocol (BT, t s detaled n [8]), our archtecture usng always an heurstc to adapt norms (2L.a) and our approach usng learnng technques (2L.b). In order to make a far comparson see [8] among BT and 2-LAMA, we have used the followng ntal norm parameters: max BW = 100%, max FR = 3. These norms are adapted at ntervals of adapt nterv =50tme steps. The learnng approach, 2L.b, uses 0.8 as the mnmum smlarty threshold (.e. MIN_SIM=0.8) and 1 as the maxmum dvergence threshold (.e. MAX_DIV=1) both values come from an emprcal study. Notce that n ths approach, assstants start wth an empty case base and use the heurstc to generate an ntal case. Later, f a problem s smlar to prevous ones, they reuse ther knowledge nstead of usng the heurstc.

7 174 J. Campos, M. López-Sánchez, and M. Esteva We have tested all three methods by varyng the peer that ntally has the datum we call round to a sngle executon wth the data n a certan ntal poston. In subsequent rounds, 2L.b s assstants already know some prevous cases snce case base s kept when sharng more data among the same agent communty. Ths process s repeated untl the data has been ntally n all peers (multple-round). Due to the random nature of the BT some served peers are selected haphazardly, the results show the average of executng a multple-round 50 tmes (.e = 600 rounds, where the 12 corresponds to all possble ntal data postons n a round, and the 50 corresponds to repeat the unque multple-round). In contrast, a multple-round does not need to be repeated when usng 2-LAMA methods, because they do not present random ssues. However, as assstants n 2L.b learn at each round, the order of ntal data postons nfluences ths approach. Thus, 2L.b results show the average of executng ths alternatve on 50 random multple-rounds (.e = 600 rounds, where the 50 corresponds to dfferent multple-rounds wth dstnct order of 12 ntal data postons). Table 1 shows the average per round of the followng metrcs: tme as the total tme requred to spread the datum among all peers; cnet whch s the network cost consumed by all messages (each message cost s computed as ts length tmes the number of lnks t traverses); h as the average number of lnks traversed by each message (hops); data as the total number of sent data messages; and cml that s the cost of all messages related wth the meta-level.e. all messages sent to or by assstants. Notce that the data metrc refers to all data messages, although some of them may not be totally transmtted f: () a destnaton peer sends a cancel message to ts source peer because t found a better source or () a peer stops sendng data to fulfl an updated normf R DL. If we compare the performance of both BT and 2-LAMA approaches, we see that our proposals requre less tme to share the datum. Ths means that t takes longer when there s no assstance despte the addtonal communcaton wth the meta-level requred by the asssted approach. In contrast, the network cost (cnet) s larger n 2-LAMA. Ths means that, n our approaches network s ntensvely used along the whole executon wthout achevng saturaton otherwse, tme would ncrease. Our proposal requres more communcaton because: () t has extra communcatons due to the meta-level, () t sends more data messages, and () t ntally measures latences to adapt DL s socal structure. Havng a meta-level () mples that coordnaton messages are exchanged among domanlevel agents and assstants and also between assstants. However, the derved network overload (cml) s small snce these control messages are very small compared wth data messages. On the contrary, () havng more data messages (data) consume a sgnfcant amount of network resources. These extra data messages are created because 2-LAMA peers compare data sources by retrevng some data from them they replace ther current data source whenever they fnd a faster one. Thus, we expect to mnmse ths network consumpton when dealng wth more than one pece of data, snce peers could compare sources dependng on prevous retreved peces. Besdes, latency measurements () represent up to a 20% of the network cost ncrement. Notce, though, that these measures are

8 A Case-Based Reasonng Approach for Norm Adaptaton 175 used to mprove system-wde data-paths by suggestng certan neghbours to each doman-level agent. Regardng the number of lnks traversed by messages (h), our approaches have more local communcatons than BT. Ths s convenent because local messages have lower latences and costs. Overall, results show that our learnng approach (2L.b) outperforms our heurstc approach (2L.a) and the BT one, snce t requres less tme. Ths means that our heurstc performs a good estmaton of the mappng between system status, norms and outcomes, but t can be enhanced. In fact, our current CBR mplementaton s already mprovng ths estmaton. 6 Conclusons Our vson s to endow the system wth adaptaton capabltes nstead of expectng ts agents to ncrease ther behavour complexty. Thus, we propose to add a meta-level that adapts a MAS organsaton as a type of assstance to the coordnaton of ts partcpants. Partcularly, ths paper apples CBR to perform such a task. It llustrates ths approach n a P2P scenaro, provdng n-depth detals about the adaptaton of norms n ths scenaro. Moreover, t emprcally compares ths approach to the BtTorrent protocol wdely used n ths scenaro. As future work, we plan to go further n CBR methodology (e.g. evaluatng solutons n a revse phase) and open MAS ssues (e.g. enterng/leavng agents). Acknowledgements. Ths work s partally funded by IEA (TIN C02-01), EVE (TIN C02-01 / TIN C02-02) and AT (CON- SOLIDER CSD ) projects, EU-FEDER funds, the Catalan Gov. (Grant 2005-SGR-00093) and M. Esteva s Ramon y Cajal contract. References 1. Repast Smphony, 2. Aamodt, A., Plaza, E.: Case-based reasonng: Foundatonal ssues, methodologcal varatons, and system approaches. AI Commun. 7(1), (1994) 3. Artks, A., Kapons, D., Ptt, J.: Dynamc Specfcatons of Norm-Governed Systems. In: MAS: Semantcs and Dynamcs of Organsatonal Models (2009) 4. Bosser, O., Gâteau, B.: Normatve mult-agent organzatons: Modelng, support and control. In: Normatve Mult-agent Systems (2007) 5. Bou, E., López-Sánchez, M., Rodríguez, J.A.: Autonomc Electronc Insttutons Self-Adaptaton n Heterogeneous Agent Socetes. In: Vouros, G., Artks, A., Staths, K., Ptt, J. (eds.) OAMAS LNCS (LNAI), vol Sprnger, Hedelberg (2009) 6. Campos, J., López-Sánchez, M., Esteva, M.: Assstance layer, a step forward n Mult-Agent Systems Coordnaton Support. In: Autonomous Agents and Multagent Systems, pp (2009) 7. Campos, J., López-Sánchez, M., Esteva, M.: Norm Adaptaton usng a Two-Level Mult-Agent System Archtecture n a Peer-to-Peer Scenaro. In: Proceedngs of COIN at AAMAS 2010 (to appear 2010)

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