A Ubiquitous Approach for Next Generation Information Systems

Transcription

1 A Ubqutous Approach for Next Generaton Informaton Systems Tarek H. El-Basuny Department of Informaton and Computer Scence, Kng Fahd Unversty of Petroleum and Mnerals, KFUPM # 37, Dhahran 31261, Saud Araba, Mal Box 413, Tel: & Fax: , Emal: helmy@ccse.kfupm.edu.sa Abstract Recently, the spread of moble technologes and communcaton nfrastructures has made the vson of ubqutous computng much more realstc and feasble. At the same tme, agent technologes have attracted a lot of nterest n both academe and ndustry as an emergng programmng paradgm. We present the system, whch s beng developed as a mult-agent-based approach that lets the users ubqutously retreve more relevant nformaton from the dstrbuted Web portals. In partcular, we developed agent-based framework, where each agent s autonomous, artculate, and socal. We reported methods to embed the autonomous portal agents nto the Web portals, to cluster the portal agents nto communtes, to explot and adapt the semantc polces by the Web mnng agent and the attrbutes of the Web portal by the portal agent adaptvely. In order to nvestgate the performance of the system, we carred out several experments and developed a smart query routng mechansm for routng the user s query. Through the experments, the results ensure that the proposed system promses to acheve more relevant nformaton to the user's queres. Keywords: Parallel and dstrbuted systems, Agent self-organzaton, Clusterng, Routng, Learnng and Adaptaton. 1. Introducton Recently, agent technologes have attracted a lot of nterest n both academe and ndustry as an emergng programmng paradgm. Wth such agent technologes, servces are created by collaboraton among agents. At the same tme, the spread of moble technologes and communcaton nfrastructures have made t possble to access the network anytme and from anywhere. Ubqutous computng plus ubqutous sensng, facltates search n ways only yet to be apprecated expand my experence wth personalzed Web search [13, 14, 15, 29]. Web search s a natural and everyday aspect of human actvty, where we are lookng for relevant nformaton to satsfy a perceved need of some nformaton.

2 The problem s that fndng the nformaton that an ndvdual desres s often qute dffcult, because of the complexty n organzaton and the quantty of nformaton stored. The models behnd current search-engnes only access the statc content of the Web, whle the Web s however hghly dynamc [22, 10]. Makng ths mmense amount of nformaton avalable for ubqutous computng n daly lfe s a great challenge. In ths envronment, to realze ubqutous systems, we propose a new agent -orented nformaton system that provdes the user wth relevant nformaton and s completely dfferent from the current search engnes populated on the Internet. Researchers n the Artfcal Intellgence (AI) and the Informaton Retreval (IR) felds have already succeeded n developng mult-agent based technques to automate tedous tasks and to facltate the management of nformaton floodng [8, 9, 11, 17, 20, 21, 26]. In ths paper we wll start by descrbng the agent system herarchy, ntroduce the novel methodologes of clusterng the system s agents nto communtes, the routng mechansm of the system and the evaluaton on the adaptablty of the system s agents. Fnally we present the expermental results and future work of the system. 2. Agent System Herarchy The basc dea behnd the work we descrbe n ths paper s to embed ntellgent agents called portal agents nto the dstrbuted Web portals as a way to partally address the problems posted by the dynamc nature of the Web. These portal agents can manfest varous levels of ntellgent behavor from smply reactve to adaptve and learnng, where agents actually learn what users lke and dslke. The mplemented system uses four basc components [Fg. 1], whch are: Interface Agent (IA), Router Agent (RA), Portal Agent (PA) and Page Mnng Agent (PMA). The IA assgned to each user s machne and s usually a runnng process that operates n parallel wth the user, communcates wth the PAs va a RA. The RA delegates the user's query to the most popular and relevant PA to the query. The PA s desgned as a specal server extenson module [27], whch learns to functon n socal envronments and where necessary collaborates, completes or negotates wth other agents. The PA creates a PMA for each Web page n the Web portal based on the preexstng Web lnks nfrastructure [9, 19], and s responsble for startng the search process and runnng of the regstered PMAs. Once started, the PMAs, whch can be seen as the local representatves of the Web pages, can access the local data of the Web pages and create the Semantc Polces (SP). At the retreval phase, the PMA uses the SP to decde whether or not the user s query belongs to the PMA. There s a clear mappng between the problem of searchng n our system and the classc AI searchng paradgm. Each PMA s a node and the hypertext lnks to other down chan PMAs are the edges of the graph to be searched. In typcal AI domans a good heurstc wll rate nodes hgher as we progress towards some goal node. In the system, the heurstc model means how a page s relevant to the gven query. The PA and the PMAs whle nteractng wth the known or down chan agents use a standard best -frst search algorthm. It has been slghtly modfed so that t wll fnsh after reachng a predefned depth value, and returns the best PAs or PMAs, whch have relevant nformaton to the user's query. We present an archtecture amed to support the semantc Web applcaton, because from the applcaton pont of vew, agents allow many of the functonaltes the semantc Web promses, agents are accessng, manpulatng, ntegratng Web content from heterogeneous resources and makng nferences about the relatonshps among lnked Web pages. However, f the Web pages have been semantcally annotated and the agents could gather semantc tags from the Web pages, then the agents would know better to search these Web pages and ther lnks. Moreover, the PA can use the gathered semantc nformaton to refne the Webcrawlng process.

3 IA 1 Router Agent Other PA IA n SP fle PA Known PAs PMAs Communty n PMAs Communty 1 PMAs Communty 2 PMAs Communty 3 Fgure 1 The herarchcal structure of system's agents 2.1 The Interface Agent The IA resdes n a user's machne and s usually a runnng process that operates n parallel wth the user, communcates wth the PAs va a RA to retreve nformaton relevant to the user's query. The IA s desgned to learn and mantan the User's Preferences (UP) ether explctly or mplctly from hs/her browsng behavor. The IA shows the results returned by the PMAs to the user after flterng and re-rankng them [12, 14]. The IA receves user's responses of hs/her nterest/not nterest to the results and regards them as rewards to the results. For the IA to be truly useful UP must be nferred mplctly from actons and not obtaned exclusvely from explct content ratngs provded by the user, because havng to stop to enter explct ratngs can alter normal patterns of browsng and readng. A more ntellgent method s to use mplct ratngs, where a ratng s obtaned by a method other than obtanng t drectly from the user. By observng the browsng behavor of the user, t s possble to nfer hs/her mplct feedback wthout requrng the explct judgments. Prevous studes have shown that readng tme to be a useful source of predctng UP mplctly [1, 2, 4, 5, 6, 7, 18, 24]. We nvestgated other sensors n correlaton wth the elapsed tme of vstng the page to make the IA detects the actual user's mplct response. We developed the IA's browser to record the user's mplct ratngs and the explct ratng of a Web page [14, 15]. Followngs are the IA s job stream: The user starts by submttng a Natural Language (NL) query to the IA. The IA analyzes the NL query usng a smple NL algorthm, throws out non-relevant words and transforms t to Q n, where Q n = k1, k 2,..., k n stands for a vector of the keywords of the query. The IA looks for relevant URLs to the Q n n the UP fles. If the IA fnds relevant URLs n the UP fles, then t shows them to the user and asks the user whether he/she s satsfed or wants to search the Web for other stes. If the IA could not fnd n ts UP fles any URLs relevant to Q n then the IA submts the query to the RA that routes Q n to a relevant PA, whch n turn forwards Q n to ts PMAs. The IA receves the results returned by the PA va the RA. The results consst of a set of Web pages and ther smlarty value to the gven query.

4 The IA takes a set of queres from the UP fles, whose smlarty to Q n s over the predefned threshold value, and creates a vector from the set of queres and Qn n order to be used for flterng the retreved results. The user explctly marks the relevant pages usng IA s feedback menu or the IA mplctly catches user s response. The response s used to adapt the content of UP fles. 2.2 The Portal Agent A PA s assgned to one Web portal to be responsble. The PA creates the hyper structure of the PMAs communtes startng from the portal address of the Web portal. The PA knows all the PMAs n the Web server and works as a gateway when the PMAs communcate wth each other or wth one n another PA. The PA ntates all the PMAs n ts doman when t starts searchng for relevant nformaton to the user's query. The PA clusters the PMAs nto communtes and automatcally defnes ts attrbutes to be used n the routng mechansm. We ntroduce a defnton of the PMAs communty that enables the PA to effectvely focus on narrow but topcally related subsets of PMAs and to ncrease the precson of the search results. A PA of n Web pages creates a SP-fle. In the SP-fle, each SP denoted by SP ( 1 n), where n s the number of PMAs, s represented as vectors of keywords sorted n alphabetcal order, SP = T jw j 1 j t, where T W are a keyword and ts weght, and j j t s the number of keywords n the SP. The weght value of the keyword decded by the frequency of the keyword and the knd of HTML tags that nclude the keyword n the Web page and s modfed accordng to the user's responses. Whle creatng a PMA for each Web page, the PA adds to ts known PAs table all the portal addresses of the external lnks, whch exst n the Web pages and pont to other Web portals. Ths means that, the PAs communty wll be created automatcally. The PMAs send "frend of mne" messages to the PA to regster the portal addresses of the external lnks n ther Web pages as known PAs Portal Agents Communty The PA wll add to ts address book all the portal addresses of the external lnks. Ths means that, the PAs communty wll be created automatcally. For nstances, whle embeddng the PA to the Web server of KFUPM f there are external lnks,.e n one of the Web pages of KFUPM s Web server, then these portal addresses wll be added as a communty member of the PA of KFUPM. Ths means, the PMAs of any PA wll send frend of mne messages [Fg. 2] to the PA to regster the portal addresses of the external lnks n ther Web pages as a relevant one. PA Known PAs PMA1 (External lnks) PMA2 (Eternal lnks) PMAn (External lnks) Fgure 2 Portal agents communty

5 2.3 The Page Mnng Agent The PMA analyzes the data that are avalable on ts Web page and contnually keeps track of any changes n the content of ts Web page. Each PMA starts wth the base address when the PMA has got t from the PA. The PMA has ts own parser, to whch the PMA passes a URL, and an SP vector n whch the PMA keeps all the polcy keywords found n ts URL. The PMA takes essental propertes and prncples gven by the PA to create the SP of the PMA as an ontology that represents the context of the Web page as follows. The PMA sets the URL of ts Web page as ts name, loads, parses the HTML content of ts Web page and extracts lnks, mages, text, headers, applets, and the ttle. Then, the PMA elmnates the nosy words (nonnformatve words), stemmng a plural noun to ts sngle form and nflexed verb to ts nfntve form. After that, the PMA creates ts SP usng an addtonal heurstcs, n whch addtonal weghts are gven to the keywords n the ttle and the headngs of the Web page. Then, the created PMA regsters tself to the PA and wrtes all the polcy keywords nto the SP fle. A SP s used by the PMA to decde whether or not the keywords n the query belong to the PMA. The SP s a vector of mportant keywords, whch are extracted and weghted by analyzng the contents of the Web page. Snce the keywords are not all equally mportant for content representaton of the SP vector of each PMA, an mportance factor λ s assgned to each keyword and decded by the knd of HTML tags, n whch the keyword s ncluded n the Web page. Ths means that the PMA wll emphasze/de -emphasze some keywords based on the value of λ. The PMA calculates the weght of the keyword and constructs ts SP vector from the number of appearance (tf) and the HTML tags, whch nclude the keyword wthn the Web page (e.g., n ttle, header, bold, talc), by usng the equaton wk = λ k tf k, where w k stands for the weght of keyword n k-th HTML tag, and tf k stands for the number of occurrences that keyword appears n k-th HTML tag. λ k stands for the weght decded by the knd of HTML tag k that ncludes the keyword n the Web page. The total weght of a keyword n the SP s the sum of all ts weghts n the HTML document of the Web page and s calculated by usng the equaton = n, where n s the number of HTML tags wthn w wk k = 1 the Web page. The PMAs calculate the smlarty between the Q n and ther Web pages based on the keywords they have n both of the SPs and the URLs of the PMAs. Ths smlarty functon s based on both Q n -SP and Q n -URL smlartes. It s a hybrd smlarty functon that ncludes two components. At the retreval phase, the PMA uses the SP to decde whether or not the user s query belongs to the PMA. The PMAs, when recevng a user's query from the PA, ntate the search process by nterpretng the user's query and/or ether askng, Is ths yours? or announcng Ths s yours to ts down-chan PMAs. The selected PMAs and/or ther down-chan PMAs of each Web server nterpret the user's query accordng to ther SPs and reply the answer Ths s mne wth a confdence value or Not mne wth zero confdence. Ths confdence value depends on the smlarty, where the smlarty s based on co-occurrence of the user's query's keywords appearng n the SPs of the PMAs Clusterng the PMAs nto Communtes Wth the ncrease of the number of Web pages n the Web servers, t be comes better to cluster the Web pages nto communtes n order to fnd quckly the desred nformaton. The PA clusters the PMAs nto communtes based on the smlarty between the SPs and the ncomng keywords of the Q n over tme n order to effectvely focus on related subsets of PMAs. The Web server's admnstrator may defne keywords as seeds for creatng the base clusters of Web pages. The name of a cluster s ntally constructed from the Q n and the most common

6 keywords n the SPs of the PMAs n the cluster and s dealt wth the man attrbutes of the cluster. The cluster's name s updated accordng to newly nput queres related to the cluster and a set of keywords surrounded by specfc HTML tags ncluded n the Web pages of the cluster and relevant to the queres. Ths means that, over tme the communtes of PMAs wll be refned so that an agent may be assgned to or released from specfc communty. We present the defnton to create a cluster of PMAs as followngs: Let Q be a set of cluster names { CNq 1 n, CNq = { w j 1 j m}}, where w j a keyword, and n s the number of elements n Q. We call the number of elements n a set, sze. Thus, n s a sze of Q, and m s a sze ofcnq. Let Q n be a user's query, where Q = { wj 1 j l}, l s a sze of Q n. n The algorthm of clusterng the PMAs nto communtes s as followngs: 1. Calculate the smlarty betwee n the keywords of the SPs. 2. Create a base cluster, each of whch ncludes a keyword and ts relatve keywords, each of whose smlarty wth the keyword s Combne base clusters whose smlarty value s over a threshold value. 4. When the user enters a query Q n, the PA checks: 5. If Q CNq = Φ for any CNq Q n j j, then creates a new cluster Cq that conssts of a set of Web pages relevant to Q n, and Q n s assgned to CNq, whch s the name ofcq,.e. CNq Q and Q Q CNq. n 6. If Q CNq Φ for each CNq Q n j j, and Q CNq, then Q Q {CNq}, n j CNq CNq Q and } n CNq CNq {k j for every k j Tag. Where Tag s a set of keywords from the content of specfc HTML tags n such Web pages that are n CNq j and relevant toq n. 2.4 Router Agent and Routng Mechansm Due to the sze and growth-rate of the Web, a good dstrbuted ndexng/searchng mechansm must be ntegrated wth a dstrbuted data -gatherng mechansm. Although a sngle router agent s scalable enough to potentally handle thousands of PAs, n practce, t s desrable to run a separate RA for relevant PAs of a common topc, for nstances, the PAs of AAAI, IEEE and ACM portals belong to one RA. The RA delegates the user's query to the most popular and relevant PA to the query to retreve the Web pages, whch are consstent wth the user's nformaton need. There s a router agent that holds a set of attrbutes that reflect the context of each PA [Table 1]. Regstered PAs Attrbutes of PAs PA 1 A 11,W 11 A 12,W 12 A 1m,W 1m PA 2 A 21,W 21 A 22,W 22 A 2m,W 2m PA n An1,W n1 An2,W n2 Anm,Wnm Table 1 Portal agent s attrbutes The PA sends ts attrbutes A j W j, whch are automatcally determned from the cluster names of the PMAs communtes n the Web portal, to the RA whle the regstraton. Where Aj

7 means the j-th attrbute of PA, and W j s ts weght value, whch assgned to each attrbute and contnually adapted by the RA over tme based on the feedback from the IAs to reflect any changes n the context of the PA. Relevancy s used to determne the popularty of the PA for a partcular type of queres. The RA mantans the relevancy S j between Q n and the attrbutes of PA j usng the equaton S j = w j, g ( k ), where g ( k ) = 1 f k exsts n both of Q n and the attrbutes of the PA j, otherwse g ( k ) = 0. There should be a sngle entty that controls the lst of RAs. Whle regsterng a RA, t goes through one of several dozens of routers who work wth, n turn, keeps a central database known as the router database that contans nformaton about the profle of each router [Fg. 3]. Each of the routers has hundred of PAs and handles ther requests. The comprom se employed by dstrbuted search conssts of a set of routers, each of whch handles the queres for a set of relevant PAs specalzed n some way. The RAs regster themselves to other routers to receve queres, whch confrm to a partcular context. The context defnes the queres that a partcular router wll expect to be sent n. When the router receves a query from the IA, t does the followngs: Looks for a lst of relevant PAs, once the RA found relevant PAs. Assgns the query to specfc PA because t already knew that ths PA s relevant to ths query, then merges the retreved results and sends them back to the IA. Forwards queres to other routers f the results do not satsfy the user or the router could not fnd a relevant PA among ts communty of PAs. It may have to do ths multple tmes. Says, there are no relevant PAs to ths query, or here s the most relevant router that may know more PAs about your query. Fgure 3 Routng mechansm

8 3. System s Agents Communcaton The system comprses the herarchcal structured agent communtes based on a PA model. A PA s the representatve of a communty and allows all agents n the communty to be treated as one normal agent outsde the communty. A PA has ts role lmted n a communty, and another hgh-level PA may manage the PA tself. A PA manages all PMAs n a communty and can multcast a message to them. Any PMA n a communty can ask the PA to do multcastng ts message. All agents form a logcal world, whch s completely separated from the physcal world consstng of agent host machnes. That means agents are not networkaware, but are organzed and located by ther places n the logcal world. Ths model s realzed wth the agent medator called Mddle-Man Agent (MMA). MMA s prmarly desgned to act as a brdge between dstrbuted physcal networks, creatng an agent communcaton nfrastructure on whch agents can be organzed n a herarchcal fashon more easly. The system s agent conssts of a communcaton unt and an applcaton unt. The communcaton unt comprses the common basc modules shared by all agents, such as the communty contactor, communcaton layer and message nterpreter. The communcaton layer transmts data from source agents/machnes to destnaton agents/machnes through networks. In the system, we smply use TCP/IP, snce most Internet applcatons mplement ther protocols on top of TCP/IP. The applcaton unt comprses a set of plug-n modules, each of whch s used for descrbng and realzng a specalzed or connatural functon of agents. 4 System's Agents Adaptaton There are several approaches that can be used to learn and adapt a UP by the IA [5, 6, 7, 28]. The IA creates a new lst of keywords K for the feedback, K f K = Q f n s. Where, K s = { t j 1 j m} s a lst of keywords pcked from the ttle and the headers of the selected URL, and Q n = { q j 1 j l} s the gven query. If one of the keywords of the K f does not exst n the query or the ttle feld s of the URLs, whch exst n the UP fles, the IA adds the new keyword wth an ntal weght reflects the current user s response. By ths way, the content of the UP fles wll evolve over tme to reflect the actual UP. The PMA allows only a relatvely small change of the weghts of SP based on the user's response, because addng/removng some keywords nto/from the SPs may change the context of the Web pages. When the user says the page s nterestng, the PMA changes the weghts of the keywords n ts SP, f and only f these keywords appear n Q n, n order to make better match wth the query to be entered next tme. Ths means that, the PMA wll emphasze/de-emphasze some keywords, frequently, reflectng the user s responses. Let n be a set of SP vectors and the user satsfed by the result retreved by the PMA. Let Q n be a user's query, such that Q = { q 1 j l}, ( l s a sze of Q n ). The PMA changes w j, the weght n j of the keyword kj n the SP fle, by addng a reward value R, so that the new weght, w ' j = w j + R δ j, where δ j = 1 f kj Q n SP and SP n, other wse δ = j 0. A weght value s assocated wth each attrbute of the PA. The weghts of the attrbutes that match the user's query are retreved from the PA's attrbutes and updated (ncreased for postve feedback or decreased for negatve feedback) by the RA based on the user's response to hs/her query routng. As the contents of the Web servers are dynamc wth new nformaton beng added, deleted, changed, and moved over tme. Therefore, the attrbutes of the PA must also be dynamc wth some attrbutes can be added or some exsted

9 attrbutes can be deleted to reflect wel the context of the Web server. If the user gets satsfed wth the retreved answer from a specfc PA whle one of the query's keywords does not exst n the attrbutes of that PA, then the RA nserts that keyword as a new attrbute wth an ntal weght that reflects the current user's response or adapt the exstence one by usng next equaton, w( t + 1) = ρ w( t) + (1 ρ) R where ρ s a heurstc value and 0 < ρ 1. 5 Expermental Results We have performed several experments to make a consstent evaluaton of system effectveness. We mean here by the effectveness, as t s purely a measure of the ubqutously of the system, the ablty of the system to satsfy the user s n terms of the relevance of documents retreved for user s queres, and the autonomously of the system s agents to adapt and learn over tme. Experment 1: n ths experment, we measured how the RA s beng able to adapt the PA's attrbutes over tme and to get good correlaton between the user's queres and the context of the Web portals. In order to understand the experment, we defne a Ftness value to show the correlaton between the weghts of the attrbutes calculated automatcally by the RA (T) and the weghts of the attrbutes calculated the system's admnstrator (S), as follows: System s admnstrator actual nterest: S j bk W, where W k k s the weght of attrbute k, = m k= 1 and bk = 1 f the admnstrator judges the keyword k n the PA j as relevant for the context of the URLs of the Web portal, else b k = 0. Adaptaton of the attrbutes by the RA: T = m j W k k= 1 We defne the Ftness value F j = Sj Tj, whch reflects the correlaton between the two adaptatons for PA j. In the experment, the user gave ffteen dfferent queres. After frequent nteractons of retreval, the admnstrator checked the correlaton of each attrbute wth the context of the Web portals and calculated S and T values, and then a Ft ness value was calculated for the attrbutes of eac h PA. The Ftness values calculated after 10 and 20 retreval nteractons are shown n [Fg.4]. Fgure 5 shows that the values of S and T are convergng over tme to each other. Ths means that the RA s beng able to predct and adapt the PA's attrbutes to reflect well the context of the Web portal's over tme.. Fttng after 10 Tmes Fttng after 20 Tmes Fttng Value PA 1 PA 4 PA 7 PA 10 PA 13 PA 16 PA 19 PA 22 PA 25 PA 28 PA 31 PA 34 PA 37 PA 40 PA 43 PA 46 PA 49 Portal Agents Fgure 4 Portal agent's attrbutes adaptablty

10 Experment 2: In ths experment, we attempted to evaluate the performance of the routng mechansm to retreve relevant nformaton to the user's queres from the PAs. We created ffty PAs that wrap ffty dfferent Web portals; the PAs create d the attrbutes and sent them to the router agent to be used n the routng process. The number of Web pages wthn the Web portals vares from 300 to 2500 pages. The users submt 30 queres to the IAs, whch n turn forward the queres to the RA. The mean number of keywords per query, ncludng the nose words, s 6.2. The RA delegates the user s queres to the most relevant PAs, receves back the results and forwards them to the IAs. Then, we calculated the precson of the retreved URLs to the users' queres as the number of relevant pages retreved dvded by the total number of retreved pages. The precson results depcted n [Fg. 5] show that the precson values vary from 0.7 to 1.0 and ths means that the routng mechansm promses to acheve more relevant nformaton to the user s queres. Precson Precson Queres Fgure 5 Precson of the queres submtted to the system Experment 3 : The topc domans of the Web pages are varous and sometmes one Web page contans several topcs at the same tme. Therefore, the relatonshp among clusters s not smple and some clusters could have close smlarty. We have mplemented the clusterng algorthm and measured how well the PA can cluster the PMAs of the Web portal nto communtes. In ths experment, the data set for the experment conssts of about 2800 Web pages. The subject feld of the experment focused to the computer scence -related Web pages. We have created PA that wraps of the IEEE (computer scence socety) Web portal. The PA creates the PMAs and the SP-fle of ths portal. The smlartes between the SP vectors of PMAs are used to create the base clusters, and then the PA uses the algorthm descrbed n secton 2.4. After frequent nteractons wth the PA of the IEEE portal where the users submtted 65 dfferent queres, the clusters were assgned by the smlarty and the threshold for the assgnment was 0.5. All the clusters were assgned f the smlarty was greater than or equal the threshold value. The results depcted n [Fg. 6] show that the system could cluster the PMAs of the IEEE Web portal nto communtes. The name of each cluster conssts of the most common keywords n the SPs of ts PMAs and the frequent keywords of the queres, whch have been well answered by the PMAs of the cluster. Examples of some Web pages, whch have been assgned nto two clusters named "IEEE Computer socety educatonal actvtes" and "IEEE conferences and publcaton", are presented. From the result, we could see that the pages n the same cluster do share smlar topc and contents under the general query topcs.

11 PMAs Clusters org/publcatons/dlb/ Fgure 6 Clusterng of the PMAs 6. Conclusons and Future Work Ths paper dscussed a mult-agent-based approach to buld a ubqutous nformaton retreval system. In ths sense, the mult-agent system s composed of possbly large number of collaboratve agents, whch collectvely try to satsfy the user s nformaton need. The paper ntroduced methods to embed portal agents nto the Web portals, to cluster the portal agents nto communtes and to route the user s queres to the most relevant PA. We created multple PAs and developed a smart query routng mechansm for routng the user's query. We carred out several experments to nvestgate the performance of system. Through these experments, we ensure that system s agents learn, adapt and clustered nto relevant communtes over tme. Currently, the routng of the system s desgned as a smple query routng that bnds to two herarchcal levels of RAs. There s a plan to scale up the system by ncreasng the number of PAs and develop more sophstcated routng mechansm for mantanng multple herarches of RAs. 7 Acknowledgments I would lke to thank Kng Fahd Unversty of Petroleum and Mnerals for fundng ths research work and provdng of the computng facltes, specal thanks to anonymous revewers for ther valuable comments on ths paper. 8 References [1] Bruslovsky, P., Tasso, C., Specal ssue on user modelng for web nformaton retreval, User Modelng and User Adapted Interacton, 14 (2004). [2] Bamshad M., Robert C. and Jadeep S. Automatc personalzaton based on Web usage mnng, Communcatons of the ACM Journal, volume 43, No. 8, pp , 2000.

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