Introducing a Multi-Agent, Multi-Criteria Methodology for Modeling Electronic Consumer s Behavior: The Case of Internet Radio

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1 Introducng a Mult-Agent, Mult-Crtera Methodology for Modelng Electronc Consumer s Behavor: The Case of Internet Rado Nkos Manousels 1, and Nkos F. Matsatsns 2 1,2 Techncal Unversty of Crete, Dept. of Producton and Management Engneerng, Decson Support Systems Laboratory, Unversty Campus, Kounoupdana, GR-73100, Chana, Greece 1 nkoslovesyou@ergasya.tuc.gr 2 nkos@das.ergasya.tuc.gr Abstract. Ths paper presents a step-by-step methodology for ntellgent systems analyss and desgn, used n modelng and supportng the decson process of the electronc consumer. Borrowng methodologes and technques from Operatonal Research and Decson Makng, detaled and accurate models of the consumer buyng and negotatng behavors are constructed. Furthermore, a mult-agent archtecture s proposed and outlned, encompassng and engagng these models n order to cooperate wth electronc marketplaces, for the needs of the consumer to be best met. Ths methodology s appled to a specfc case of study, the case of the Internet rado lsteners. 1 Introducton In order to model the behavor of a consumer a lot of dfferent and often non-quanttatve parameters have to be measured; the contrbuton of a mult-crtera approach n analyzng consumer s preferences s thus nvaluable. Research regardng answers to these matters s under development by several research teams throughout the world. The deas outlned by the Software Agents Group of MIT are very attractve, as far as modelng of electronc consumers behavor s concerned (Guttman et al. [4], Moukas et al. [8], Maes et al. [7], Chavez & Maes [2]). Moreover, Terpsds et al. [10] propose an agent-based re-engneerng modelng process for electronc commerce havng n mnd the way electronc commerce wll affect retal market behavor; the gudelnes they are defnng for modelng the Consumer Buyng Behavor generally adopt Marketng concepts. Most of these approaches show the necessary attenton towards the mult-attrbute aspect of modelng the needs of electronc consumer, but we beleve there s stll work to be done n the area of equppng the medatng agents wth robust and dynamc (not statc) models of the consumer buyng and negotaton behavors. In ths paper we are gong to descrbe a new approach to ths problem concernng electronc consumers, borrowng technques and methodologes from the scences of Operatonal Research and Decson Makng. The preference dsaggregatng analyss technques are a perfectly adaptve tool n creatng a behavor model of the consumer, based on the crtera taken under serous consderaton when he/she chooses a product. The case under study s gong to be that of an Internet rado lstener, who makes decsons regardng the selecton of the rado staton he prefers, dependng on varous crtera that affect hs choce. The model of the rado lstener cannot be easly approached by the classc methods of marketng because hs characterstcs vary through tme and locaton, and the credtablty of the personal profle he mposes cannot be safely verfed. The way to avod statstcally descrbng (and hence locatng) a potental consumer s smple: montor each specfc lstener s tastes and habts. The paper s organzed as follows. Secton 2 presents the defnton of the decson modelng process of the problem. In Secton 3 the agent-based modelng methodology s presented and appled. Secton 4 summarzes and concludes the basc concepts of the methodology; t also outlnes some basc gudelnes for future expansons of the methodology 2 Decson Problem Modelng Consumer Behavor can be defned as the decson process and acts of people nvolved n buyng and usng process. The general methodology of decson makng-problems ncludes four modelng steps, begnnng wth the defnton of the object of the decson and endng wth the actvty of decson ad [9]. Ths methodology s closely followed n our procedure of defnng the model of the decson problem of the Internet rado lstener. The proposed steps are the followng:

2 . Defnng the object of decson, that s the set of potental actons (products or servces) and the problematc of the decson. That ncludes defnton of the decson varables n form of a consderaton set A and defnton of whether we want to rank, sort, choose one or smply comment on the acton ncluded n A.. Studyng the parameters nfluencng decson and defnng a set of crtera that wll help to model the consumer behavor. Ths step regards modelng a consstent famly of crtera, assumng that these crtera are non-decreasng value functons, exhaustve and non-redundant.. Choosng an approprate mult-crtera aggregaton method. The decson maker s asked to express hs global preferences on a reference set A R, takng nto consderaton the evaluatons of the reference actons n A R on all crtera. When then use ths prevous experence (expressed by the evaluatons on the actons n the reference set) to predct the evaluatons of the actons belongng n the real set A [5]. v. Implementng the decson ad system, accordng to the nfrastructure proposed. To acheve ths goal, we bult an agent-based survellance system that montors the consumer s preferences and behavor, and creates a dynamcally changng model of ths behavor whle nteractng wth hm va a sutable desgned graphcal envronment. These steps are descrbed n detals, at the followng sub-sectons. 2.1 Object of Decson, Potental Actons and Problematc The decson concerns the choce of an Internet broadcastng rado staton, therefore the set of potental actons s the choce of one or more of the avalable broadcastng rado statons - that s the defnton of a sub-set B from the orgnal set A of the avalable actons. Each consumer makes a choce of a certan number of actons (a.k.a. rado statons) and defnes strctly the preferable number of actons to be ncluded n set B. Therefore, we conclude that the problem regards rather rankng than sortng of the potental actons (problematc γ [9]). Then the frst N actons are selected. 2.2 Famly of crtera The next step s modelng a consstent famly of crtera, assumng that these crtera are nondecreasng value functons, exhaustve and non-redundant. Each crteron s defned on A as t follows: g : A [ g, g ] R / a g( ) R a, (1) where [ g, g ] s the crteron evaluaton scale, wth g the worst level of the th crteron, g the best level of the th crteron, (a) the evaluaton or performance of acton a on the th crteron and g g(a) the vector of performances of acton a on the n crtera [5]. A consstent famly of crtera n ths case of study should contan only qualtatve ones and fully descrbe the Internet rado staton as a product or servce, accordng to the judgment of the analyst (.e. speech, advertsements, expermentaton, qualty of the sgnal and maxmum number of lsteners connected). 2.3 Preference model There are many dfferent methods of mult-crtera analyss whch can be recommended dependng on the crcumstances of decson makng. A well-known method, belongng to the subset of total aggregaton methods that compute trade-off coeffcents between crtera, s the UTA method. We chose to use the UTA method as the preference modelng method, so let us brefly ntroduce some of ts basc deas. The UTA method [5] ams at nferrng one or more addtve value functons from a gven rankng R on a reference set of actons A R. The method uses specal lnear programmng technques to assess these functons so that the rankngs obtaned through these functons on A R are as consstent as possble wth the gven one. In ths way we obtan closely optmal modelng of the consumer s behavor, takng both quanttatve and qualtatve crtera under consderaton. The crtera aggregaton model n UTA s assumed to be an addtve value functon of the followng form:

3 n u( g) = p u ( g ), (2) = 1 subject to normalzaton constrants u ( g n ) = 0, u ( g = 1 p ) = 1, = 1,2,..., n = 1 (3) where u (= 1, 2,, n) are non-decreasng real valued functons (named margnal value or utlty functons and normalzed between 0 and 1), g, g are the lower and upper bounds respectvely for the value of each crteron, and p s the weght of u. The UTA method has several nterestng features [1]: t makes possble the estmaton of a nonlnear addtve functon whch s obtaned by the use of a lnear program that provdes convenent pecewse lnear approxmaton of the functon, and the only nformaton requred from the decson maker are the global stated preferences between the dfferent actons of the reference set A R. We have to note here that preference aggregaton methods lke UTA are generally sutable for use when there are dffcultes n drectly obtanng from the user the values of the preference model. In most smple cases [3], t may be better for a user to just express the attrbute preferences rather than spend tme makng a seres of product choces whch wll (at best) nfer the same preferences. We chose UTA n the case of e-commerce applcatons, snce t generally copes well wth nosy or nconsstent data [1], t s conducve to changes n product preferences and ts not as tme-consumng, redundant and borng as the drect defnton of each preference attrbute for each category of products. 2.4 Decson ad In order to support the decson maker (hence the electronc consumer) to make hs/her selecton, we propose a mult-agent based methodology. The basc dea of ths method s creatng one behavor model for each consumer enterng an electronc market, and not just smulatng hs behavor usng statstcal tools as t s done usng the classc methodologes. The way ths s acheved by the multagent system s presented here:. The decson system obtans from the consumer a frst evaluaton of a test set of products and a weak-order rankng of them. The system also gathers nformaton regardng the negotatng atttude of the user. The user ndcates how flexble he/she s on the value of each crteron used n descrbng a rado staton.. The system uses the UTA method n order to evaluate the margnal and global utltes, creatng thus a model of the consumer s behavor and a model of the negotatng atttude and trade-offs [6].. The system electroncally checks new compettve products enterng the target market, and proposes to the consumer those fttng hs model. Durng the search n the electronc market, the system uses the negotatng parameters defned by the user, as gudelnes for the searchng and negotaton procedures. v. The system montors the reacton of the user on these proposals, by montorng changes of the personal rankng of preferred products and of the way he/she explans the satsfactory or nonsatsfactory ponts of the products proposed. v. Whenever a change of rankng occurs, the model of the consumer s re-constructed. In order to analyze and desgn ths system, we utlze a methodology proposed by Wooldrdge et al. [11]. Ths methodology s founded on the vew of the system as a computatonal organzaton consstng of varous nteractng roles, and t deals wth both the macro-level (socetal) and the mcrolevel (agent) aspects of systems. Both the methodology and the way t s mplemented n our case of study are presented n the next secton.

4 3. Agent-Orented Analyss and Desgn The methodology proposed by Wooldrdge et al. [16], allows an analyst to go systematcally from a statement of requrements to a desgn that s suffcently detaled to be drectly mplemented. The man agent-specfc concepts ths methodology provdes, can be dvded nto two categores: abstract and concrete. Abstract enttes are those used durng analyss phase to conceptualze the system, but whch do not necessarly have any drect realzaton wthn the system. Concrete enttes, n contrast, are used wthn the desgn process, and are amed to have drect counterparts n the run-tme system. The objectve of the analyss phase s to develop an understandng of the system and ts structure, wthout reference to any mplementaton detal. The analyss stage s summarzed n the followng steps:. Identfy the roles n the system.. For each role, dentfy and document the assocated protocols. Protocols are the patterns of nteracton that occur n the system between the varous roles.. Usng the protocol model as a bass, elaborate the roles model. Through the desgn process, our am s to transform the analyss models nto a suffcently low level of abstracton that tradtonal desgn technques may be appled. The desgn process nvolves generatng three models:. Create an agent model, by aggregatng roles nto agent types, refnng to form an agent type herarchy and documentng the nstances of each agent type usng nstance annotatons.. Develop a servces model, by examnng protocols and safety and lveness propertes of roles.. Develop an acquantance model from the nteracton model and agent model. 3.1 Analyss phase Followng the above descrbed methodology we frst go through the analyss phase of the system. Throughout ths phase, three man models have to be defned: the prototypcal roles model, the nteracton model and the full roles model. Fg. 1. Interactons Model (protocol defntons) The prototypcal roles needed n the agent-based DSS are manly four, snce there are four dfferent categores of actons to be elaborated. Frst an entty must nteract wth the user, gather nformaton needed from hm/her, and present the results of all the undergong procedures. A second entty needed s one to montor user s actons, wthout drectly nteractng wth hm/her, and wthout allowng nterferences wth the ngong procedures. The thrd entty has to be nvolved wth the constructon of the behavor models usng the nformaton provded by the prevous two enttes, and by utlzng the UTA method. Fnally, one last entty s needed n order for the system to communcate wth the outsde world, hence the electronc market. Ths communcaton entty sends the consumer behavor profle of

5 the user to agents utlzed n the electronc market and receves sets of new actons matchng ths profle, n order to be proposed to hm/her. All the nteractons between the roles descrbed above, are clearly depcted n the dentfcaton and documentaton of the assocated protocols, n the nteracton model of Fgure 1. After defnng the protocols assocated wth each role, t s possble to create a fully detaled schema for each role, contanng ts descrpton, and the permssons and responsbltes attrbutes assocated wth t. 3.2 Desgn Process The purpose of the agent model s to document the varous agent types that wll be used n the system and the agent nstances that wll realze these agent types at run-tme. As t s depcted n Fgure 2, three agents are requred for the mplementaton of the proposed archtecture. These three agents (namely James, Mr Q and Manypenny) ncorporate the necessary roles, as shown. More specfcally: - James: the structure of ths agent encapsulates the functons of the two roles that demand nteracton wth the user, ether drectly or ndrectly. These roles are InteractWthUser (performng the drect nteracton wth the user) and MontorUser (performng the ndrect nteracton wth the user). - Mr Q: ths agent s responsble for utlzng the mult-crtera algorthms, n order to create the behavor models. Ths agent type corresponds to the Model Creator role and can exst n the system n mostly two nstances: creatng the consumer and creatng the negotatng behavor model. - Manypenny: ths agent s responsble for gatherng the necessary nformaton from the agent system (e.g. the behavor models) and formulatng t n a comprehensve from the electronc market form. Smlarly, the ncomng messages (e.g. new proposed rankngs) are translated and forwarded to the regarded agents of the model. Fg. 2. Acquantance model ncorporatng the Agent model It should be noted that mplementaton detals regardng the tools used by the agents (e.g. the multcrtera methods used n the models creatons or the communcaton and negotaton templates) are ndependent from the nfrastructure of the agent model, and from the archtecture proposed. It s obvous then that these mplementaton detals or characterstcs can be dynamcally altered wthout nfluencng the valdty of ths model. 3.2 Methodology Applcaton The proposed methodology s mplemented and tested n a prototype mult-agent testbed. The prototype system mplements nteractons takng place between the agents of the customer-based decson support system, and also smulates the envronment of an electronc market, where the representatves of the customers communcate wth a great number of merchants makng dfferent mult-attrbute proposals. Several negotaton models are also tested and the methodology has proven to provde remarkable results [6], especally n cases where the customer s choosng several products from a very large number of avalable proposals. The user s agents use a mult-attrbute preference model n order to flter the proposals and select the most nterestng ones.

6 4. Conclusons and Future Work As t s stated n [4], there are several lmtatons n modelng consumer behavor. For example, research focuses prmarly on retal markets (although many concepts pertan to busness-to-busness and consumer-to-consumer markets as well). Even wthn retal, not all shoppng behavors are captured (e.g. mpulse purchasng). Also, electronc commerce covers a broad range of ssues, some of whch are beyond the scope of a consumer behavor model (e.g. back-offce management and other merchant ssues). Nevertheless, the consumer behavor model s a powerful tool to help us understand the roles of agents as medators n electronc commerce. In ths way, our methodology provdes the analyst wth a fully descrbed method of mplementng a mult-agent consumer behavor modelng system. Ths methodology ncludes aspects of the analyss not addressed n other approaches, lke encompassng both buyng and negotatng models when modelng the consumer behavor. The models can be created usng the descrbed UTA method or wth every other approprate mult-attrbute modelng method, wthout the system valdty beng affected. There s stll enough work to be carred as far as the proposed methodology s concerned. For example, parameters lke choce of detaled negotaton tactcs or use of reputaton mechansms are not addressed here. Moreover, ntegrty rules for the overall system are not yet fully specfed. A compatble electronc marketplace should be fully deployed, n order to fne-tune the characterstcs of the cooperatng systems. Regardng the applcaton of the methodology n the specfc case of Internet rado lsteners, there s stll work to be done n modelng mportant detals of the consumer buyng and negotatng behavor. As we noted n Secton 2, the enhanced model should nclude specal crtera, dependng on the judgment of the system analyst (e.g. musc content, or news vs. fun orentaton). Moreover, negotaton parameters should defntely nclude a reputaton mechansm, snce lsteners tend to form strong group allances, wth smlar tastes. All of the above are under consderaton and wll be ncluded nto the prototype verson of the rado lstener adng system. References 1. Beuthe, M., Scannella, G.: Comparatve analyss of UTA multcrtera methods. European Journal of Operatonal Research, 130, Chavez, A., Maes, P.: Kasbah: An Agent Marketplace for Buyng and Sellng Goods. Proceedngs of the Frst Internatonal Conference on the Practcal Applcaton of Intellgent Agents and Mult- Agent Technology, London, UK, Aprl Guttman, R., Maes, P.: Agent-Medated Integratve Negotaton for Retal Electronc Commerce. Proceedngs of 8 th MAAMAW 97, Sweden, May Guttman, R., Moukas, A., Maes, P.: Agent-medated Electronc Commerce: A survey. Knowledge Engneerng Revew, June Jacquet-Lagreze, E., Sskos, Y.: Preference dsaggregaton: 20 years of MCDA experence. European Journal of Operatonal Research, Vol. 130, pp , Karamppers, P., Manousels, N., Matsatsns, N.F., Sskos, Y.: UTAGREE: Mult-Attrbute Utlty Models for Automated Negotaton. Paper under revew. 7. Maes, P., Guttman, R., Moukas, A.: Agents that Buy and Sell: Transformng Commerce as we Know It. Communcatons of the ACM, March Moukas, A., Guttman, R., Maes, P.: Agent-medated Electronc Commerce: an MIT Meda Laboratory Perspectve. Proceedngs of the Internatonal Conference on Electronc Commerce, Roy, B.: Multcrtera Methodology for Decson Adng. Kluwer Academc Publshers, Terpsds, I., Moukas, A., Pergoudaks, B., Doukds, G., Maes, P.: The potental of Electronc commerce n re-engneerng consumer-retaler relatonshps through Intellgent Agents. In: J.-Y. Roger et al. (eds.): Advances n Informaton Technologes: The Busness Challenge. IOS Press, Wooldrdge, M., Jennngs, N., Knny, D.: A Methodology for Agent-Orented Analyss and Desgn. Proceedngs of 3 rd Internatonal Conference on Autonomous Agents, Seattle, pp , 1999.