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Engineering Applications of Artificial Intelligence 21 (2008) 633–643

www.elsevier.com/locate/engappai

An efficient multilateral negotiation system forpervasive computing environments

Sanghyun Park�, Sung-Bong Yang

Department of Computer Science, Yonsei University, Seoul 120-749, Republic of Korea

Received 22 August 2006; received in revised form 3 June 2007; accepted 16 July 2007

Available online 29 August 2007

Abstract

In this paper we propose an automated negotiation system that can efficiently carry out multilateral negotiations with multi-attributes

in pervasive computing environments. For the multilateral negotiation system proposed in our study, an e-commerce framework for

pervasive computing environments in which the components can dynamically join and disjoin a virtual market is also introduced. This

framework overcomes the locality of pure ad hoc networks and supports efficiently multilateral negotiation models in pervasive

computing environments.

In order to achieve the applicability toward multilateral negotiations, the concept of a mediator agent and the bilateral negotiation

scheme based on linear programming is utilized for the proposed system. The experimental results show that the proposed system

produces higher joint profits in negotiations about 5% and is about 96 times faster in reaching agreements on the average under the

condition of agreement for reciprocity than a negotiation system based on the trade-off mechanism.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Multilateral negotiations; Electronic commerce; Pervasive computing; Linear programming; Jini architecture

1. Introduction

Automated negotiations among multiple participantshave been researched as one of the prominent fields in e-commerce (Kraus, 2001; Jennings et al., 2002). Mostnegotiation systems in e-commerce, however, have notbeen evolved sufficiently at a level of robust systemsapplicable to commercial web sites, especially to the field ofmultilateral negotiations. Such underdevelopment is due tothe fact that the attributes, which may influence thebehavior of the participants, cannot be defined preciselyand that the success of a negotiation is dependent not onlyon the price but on other factors such as diverse personalinclination of the participants. Moreover, multilateralnegotiations are more complicated and more time con-suming than bilateral negotiations due to the considerationof multilateral matching among the participants.

e front matter r 2007 Elsevier Ltd. All rights reserved.

gappai.2007.07.005

ing author. Tel.: +822 2123 2717; fax: +82 2 365 2579.

ess: psh@cs.yonsei.ac.kr (S. Park).

In the earlier studies of automated negotiations, Sycara(1989) proposed the persuader system that utilizes theconcept of argumentation and mediation. It had beenmodeled by considering an iterative negotiation andmultiple issues. But it has no concern about the fact thatit is not necessary for the agents to have similar beliefs atthe end of negotiations, because the persuasion mechan-isms operate on the beliefs of agents with the aim ofchanging one or both parties’ beliefs. Chavez and Maes(1996) presented a negotiation system called Kasbah thatexploits certain negotiation strategies for the real-worldapplications. In Kasbah, however, a negotiation is carriedout over a single attribute such as price and the number ofstrategies that can be adopted by the agents is limited toonly three and even their selections are not autonomous.Faratin et al. (1999, 2000) proposed several negotiationsystems that are based on the algorithms of structuredinteractions among the participants in negotiations. Thesenegotiation systems are capable of dealing with multipleattributes in bilateral negotiations and of reaching agree-ments through interactions among the agents. The bilateral

ARTICLE IN PRESSS. Park, S.-B. Yang / Engineering Applications of Artificial Intelligence 21 (2008) 633–643634

negotiation systems in Faratin et al. (1999), however, didnot seem to consider the execution time in reachingagreements for negotiations and their applicability towardmultilateral negotiations. For adaptive agent-based nego-tiations, Oliver (1996) showed that agents could learnstrategies using a genetic algorithm-based learning techni-que and Oprea (2002) suggested a negotiation system thatuses a feed forward artificial neural network as the learningability of a negotiation model in the context of agent-basede-commerce. These studies have shown satisfying results onbilateral negotiations under long-term deadlines. But theirsystems do require longer time intervals to obtain betterprofits. Kurbel and Loutchko (2005) proposed a multi-lateral negotiation model of agents with fuzzy constraintson an electronic marketplace for personnel acquisition.However, multi-issue negotiation with fuzzy constraints onboth parties requires more information from a participantand makes it more difficult to explain the strategies to theparticipant. That is, the more intelligent the agents are, themore complex user interface is required. Lopes et al. (2004)presented a generic agent negotiation model for multi-party and multi-issue. Their model paid attention to theproblem of integrating an individual behavior with anegotiation model and focused on the negotiation strate-gies that are motivated by human negotiation procedures.However, their negotiation model does not consider theenvironments of negotiations such as ad hoc networks orubiquitous computing networks. Moreover, they plan toperform the experimental validation of the model whichhandles two-party multi-issue negotiation as future work.As the technologies advance, the e-commerce environmentshave been diversified from the traditional wired and/orwireless networks to short range ad hoc networks, and theyare shifting towards the pervasive computing environments(Ratsimor et al., 2004). An ad hoc network can be definedas a network without central controls and connections tothe outside world. By pervasive computing we mean thatcomputing power is available everywhere at any time. Ine-commerce, for pervasive computing environment, dy-namic networking such as numerous and unpredictableconnections and/or disconnections of participants shouldbe allowed, and the limitation of the physical area in an adhoc network should be overcome. Therefore, a compoundnetwork consisting of wired and/or wireless networks andad hoc networks is appropriate for a pervasive computingenvironment in e-commerce. In a pervasive computingenvironment, an automated negotiation system requiresefficient negotiation processes and the facilities of dis-covering services (Shenoi et al., 2006). With regard toservice discovery in a pervasive environment, Ratsimoret al. (2004) proposed a policy-driven service discoveryframework suitable for ad hoc networks. Their frameworkcan avoid common problems, such as leader selection,associated with structured compound formation. However,the emerging e-commerce, including mobile commerce,cannot be based only on pure mobile networks, especiallyon ad hoc networks. In the recent work of Kurkovsky and

Harihar (2006), they built a fully functional prototype ofthe framework implementing the interactive mobile mar-keting. They proposed an adaptive and personalizedm-commerce application designed to deliver targetedpromotional information to the participants. They alsopresented a study demonstrating end-user usability of theframework. However, their framework is based andfocused on interacting between a buyer with a mobiledevice and a server installed at every participating retaillocation providing information about the products relevantto buyer’s shopping preferences. It is close to a context-aware recommendation system for mobile users rather thanan automated trading system in the pervasive computingenvironments.In this paper, we propose a negotiation system that can

carry out multilateral negotiations efficiently with multi-attributes for pervasive computing environments. Theproposed system adopts the concept of confidentialmediators for guaranteeing fairness in negotiations. Amediator receives offers from buyers and sellers, whilebuyers and sellers do not expose their offers to theircounterparts. The proposed multilateral negotiation systemis based on a bilateral negotiation scheme in which a linearprogramming model is exploited for finding a buyer–sellerpair with better profits. For the proposed system, we alsointroduce a framework for a pervasive computing environ-ment in which the components can dynamically join anddisjoin a community network. This framework overcomesthe locality of pure ad hoc networks and supportsefficiently multilateral negotiation models in pervasivecomputing environments. In order to evaluate the perfor-mance of the proposed system, the bilateral negotiationsystem presented by Faratin et al. (2000) has beenimplemented. In addition, we have extended Faratin’ssystem so that it can perform multilateral negotiations. Theexperimental results show that our system produces higherjoint profits in negotiations about 5% and carries outabout 96 times faster on the average than Faratin’s system.The remainder of the paper is organized as follows. In

Section 2, we introduce an e-commerce framework for apervasive computing environment. Section 3 presents amultilateral negotiation system for a virtual market, whichis based on the framework proposed in Section 2. Section 4describes the simulation environment of a trading modeland the experimental data sets for the participants in anegotiation. Section 5 provides the experimental results.Finally, the conclusions and future work are given inSection 6.

2. An e-commerce framework for a pervasive computing

environment

2.1. An overview of a system structure

In this section we introduce an e-commerce frameworkfor a pervasive computing environment. Fig. 1 illustratesan e-commerce structure with dynamic federations of

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Mobile

Device

PLUS

Federation AFederation B

Federation C

Utility Agent

Layer

Service

Repository Layer

InfrastructureLayer

PLUS

Static

DevicePLUS

PLUS

PLUS

Fig. 1. An e-commerce structure with dynamic federations of lookup services for a pervasive computing environment.

Service

Connection

Agent

Federation

Agent

Service List

Service Service

RepositoryRepository

LayerLayer

Location List

Lookup Service

Utility AgentUtility Agent

LayerLayer

Infrastructure Infrastructure

LayerLayer

Virtual Machines

Networking / Resources

Fig. 2. The framework of a PLUS.

S. Park, S.-B. Yang / Engineering Applications of Artificial Intelligence 21 (2008) 633–643 635

lookup services in a pervasive computing environment. Bydynamic federation we mean that the members of afederation are dynamically determined according to theproperties of the requested services such as the item oftrading. In order to find other lookup services, a peer-to-peer (P2P) network is adopted as a communicationbackbone because of its scalability of networking. Wenamed our framework ‘P2P-based lookup server’, a PLUSfor short. In the system structure of Fig. 1, each PLUS canfederate dynamically with other PLUSs. A PLUS providesits own services for other PLUSs within the samefederation, and requests services to them as well. A PLUSmay become a member of other federations simulta-neously. A PLUS has a local service community consistingof mobile and/or static devices. Such devices may includePDAs, smart phones, laptop computers, desktop compu-ters, etc.

In this structure, a task agent on a device plays as asurrogate for its corresponding negotiation participantsuch as a buyer/seller or a mediator. The scope of thestructure as shown in Fig. 1 may be composed of a sub-network of a LAN, a LAN itself, or a WAN. In this paper,we apply PLUS to a multilateral e-commerce systemalthough it can be applicable to various pervasivecomputing systems such as u-hospital (Carrillo-Ramoset al., 2005).

2.2. The framework of a PLUS

A PLUS consists of three layers—the utility agent layer,the service repository layer, and the infrastructure layer asshown in Fig. 2. The utility agent layer of a PLUS assiststhe task agents on the devices to connect with the PLUSand to register and/or receive the services. For these

purposes, in the utility agent layer there are two utilityagents, the service connection agent and the federationagent. The service connection agent registers the services ofsome task agents (as the service providers) into the servicerepository layer of the same PLUS and provides theservices for some task agents (who have requested services)of the devices. The service connection agent maintains a listof services available in the PLUS, called the service list, andsearches its service list to provide the task agents with theirrequested services. When the service connection agent failsto find a requested service, it asks the federation agent tofind it. The federation agent keeps its own location listwhich has the location information such as the URLs of

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other PLUSs and searches other PLUSs listed in itslocation list for the service. When a requested service isnot available in any PLUS listed in the location list, thefederation agent can request a service to neighboringPLUSs though a P2P protocol. The location informationof newly found PLUS is added into the location list by thefederation agent. Another main role of the federation agentis to make a federation with other PLUSs. The services in afederation are available to all the task agents on the devicesin the same federation.

The service repository layer provides the lookup servicesfor the service connection agent in the utility agent layer.Any of the lookup services such as Jini, universal plugand play (uPnP), and service location protocol (SLP)can be adopted for this framework. The service repositorylayer finds and registers the services that the serviceconnection agent requests. The infrastructure layer is basedon the platforms such as virtual machines, hardware fornetworking, or the hardware resources of the PLUSframework.

3. A negotiation system based on PLUS

3.1. Evaluating the profits

The profits of the participants, buyers or sellers, arequantified with numerical values in order to measure thedegree of satisfaction of the participants. In this paper, themulti-attribute utility theory (MAUT) (Barbuceanu andLo, 2000) is applied to evaluate the profits of theparticipants, considering multiple attributes of the mer-chandise. Each attribute of the merchandise has a weightindicating a relative preference to each of other attributes.The utility function in MAUT is expressed in terms of theweights of the attributes and the evaluation function at theoffer values of the attributes, where the offer value of anattribute is defined as the value that a participant offers forthe attribute in a contract. Therefore, a contract of aparticipant that consists of the offer values of all theattributes can be regarded as a ‘proposal’ in the real-worldnegotiations. The value of the utility function can beregarded as the profit of either a buyer or a seller. Theutility function Profits(xiÞ of a participant can be expressedas follows:

ProfitsðxiÞ ¼Xn

i¼1

wi � EðxiÞ;Xn

i¼1

wi ¼ 1, (1)

where n is the number of attributes, xi is a variablerepresenting the offer value of the ith attribute, wi is theweight of the ith attribute, and finally the evaluationfunction EðxiÞ of the ith attribute is expressed in terms ofthe request values (request_valuei) and the allowable values(allowable_valuei) as follows:

EðxiÞ ¼xi � allowable_valuei

request_valuei � allowable_valuei

, (2)

where the allowable value means the maximum value towhich the participant concedes for negotiation, and therequest value means the maximum value the participantwants in negotiation. For example, for the price attribute ofa product, if a buyer wants to pay $800 for the product, yetthe buyer may pay up to $1000 for it, then the requestand the allowable values for the buyer are $8000 and$1000, respectively. In Eq. (2), allowable_valuei andrequest_valuei are the allowable and the request values ofthe ith attribute, respectively. If xi ¼ request_valuei, thedegree of satisfaction of the ith attribute is assumed to bethe highest, and EðxiÞ representing the degree of satisfac-tion of the ith attribute becomes 1. On the contrary ifxi ¼ allowable_valuei, the degree of satisfaction of theith attribute is the lowest, and EðxiÞ is set to 0. Therefore,when xi is within the range between request_valuei

andallowable_valuei, EðxiÞ ranges between 0 and 1. If xi

is out of the range between request_valuei andallowable_valuei, EðxiÞ is set to either 0 or 1 dependingon the value of EðxiÞ. That is, if EðxiÞo0, it is set to 0, andif EðxiÞ41, it is set to 1.

3.2. A simple formulation of a bilateral negotiation

We propose a model of a bilateral negotiation consider-ing the efficiency of a negotiation and the extensibilitytoward multilateral negotiations. In the proposed bilateralnegotiation model, the concept of confidential mediators isadopted for guaranteeing fairness in negotiations. Amediator agent receives the negotiation information fromthe participants, a buyer and a seller, in a bilateralnegotiation and carries out a negotiation between a buyerand a seller who have the common negotiation ranges(CNRs) of all the attributes of the product. The negotiation

range of the ith attribute means the range betweenrequest_valuei and allowable_valuei for a participant andthe CNR of a seller and a buyer for an attribute denotes therange overlapped between the negotiation ranges of bothparties for the attribute. A mediator agent can finally findan agreement for reciprocity between both parties. Reci-

procity means the reciprocal profit between a buyer and aseller, and an agreement for reciprocity means an agreementthat is not partial to one participant over the other within asmall allowable range.In this paper, we exploit linear programming to solve the

problem of finding an agreement for reciprocity in abilateral negotiation. The objective function for a linearprogramming is established by maximizing the joint profit

which is the summation of the profits of both a buyer and aseller, because each bilateral negotiation is to be carried outunder the conditions of agreement for reciprocity, andbecause the value of the profit of a participant is anonnegative real number between 0 and 1. Note that otheroperators like the multiplication of both profits may notreturn proper values for evaluating an agreement. Theconstraint condition is that the difference between theprofits of the buyer and the seller should be smaller than a

i

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JVM

ServiceConnection

Agent

Federation

Agent

ServiceList

LocationList

Jini LUS

Virtual Market (PLUS)

Mediator

Agent

Seller

Agent

Buyer

Agent

Seller

Agent

Buyer

Agent

Buyer

Agent

Buyer

Agent

Mediator

Agent

Fig. 3. A schematic diagram of the proposed multilateral negotiation

system based on PLUS.

S. Park, S.-B. Yang / Engineering Applications of Artificial Intelligence 21 (2008) 633–643 637

threshold value, denoted as d. For the sake of fairness forboth participants, the value of d would better be as small aspossible. However, as the value of d gets smaller, thepossibility of existence of the agreements which satisfy thepossibly tight conditions on negotiations gets lower andlower. The performance of solving a linear programmingmodel in the proposed system is not affected theoreticallyby any variations on the threshold value, and it is a matterof choice for the user of the negotiation system to select aproper threshold value according to the characteristics ofnegotiations. In this paper, considering the meaning ofreciprocity and the agreement ratio of negotiationssimultaneously, the threshold value is set to 0.01 for thenegotiation systems, which means that the partiality of theprofits between a buyer and a seller is not greater than 1%.The boundary conditions in a negotiation can be decidedby the CNRs of all the attributes of the product. Thefollowing describes a linear programming model of theproblem of finding an agreement for reciprocity:

Procedure: The linear programming formulation of abilateral negotiation.

The objective function:

Maximize z ¼ ProfitsbuyerðxiÞ þ ProfitssellerðxiÞ. (3)

The constraint conditions:

jProfitsbuyerðxiÞ � ProfitssellerðxiÞjpd. (4)

The boundary conditions:

The lower bound of CNRipxipthe upper bound of CNR

ði ¼ 1; 2; . . . ; nÞ, ð5Þ

where z is the optimal objective value, d indicates thethreshold for reciprocity, n is the number of attributes ofthe product, ProfitsbuyerðxiÞ and ProfitssellerðxiÞ representthe profits of the buyer and the seller, respectively, andCNRi, the CNR of the ith attribute, can be expressed asfollows:

CNRi ¼ fNegotiation_rangei for buyerg

\ fNegotiation_rangei for sellerg. ð6Þ

Recall that the negotiation range of the ith attribute,Negotiation_rangei, means the range between request_valuei and allowable_valuei for either a buyer or a seller.

From the above linear programming model, a solution,the set of variables xi, satisfying the above conditions isan agreement for reciprocity in a bilateral negotiation.If an offer value is expected to be an integer (e.g. the priceof a product), the offer value can be rounded off thefraction.

3.3. The proposed multilateral negotiation system

3.3.1. The components of the proposed multilateral

negotiation system

The negotiation system for a pervasive computingenvironment consists of a virtual market and task agents

on the devices of the participants. In the system, the role ofa task agent is a surrogate of a participant such as a buyer,a seller, or a mediator. A virtual market in our study is aplace where the client agents—buyer and seller agents—canfind appropriate mediator agents, and where the mediatoragents register their proxies. A virtual market can beimplemented with a PLUS as shown in Fig. 3. In the utilityagent layer of a PLUS, the utility agents are implementedon Java Agent DEvelopment framework (JADE), which isone of the foundation for intelligent physical agents (FIPA)compliant platforms and is a convenient tool for agentapplication development. The service repository layer of avirtual market has been implemented to the Jini architec-ture (Waldo, 2001) although other service discoveryarchitectures such as uPnP and SLP may be adopted inthe framework. The Jini architecture is a simple infra-structure for providing arbitrary services in a network andfor creating spontaneous interactions between the objectsthat use these services. In the Jini protocol, both a serviceprovider and a service requester can find dynamically thelocation of the lookup service (Jini LUS) through adiscovery protocol. In addition, Jini can support thedevelopment of a protocol-independent distributed system.Service proxies can communicate with a service providerwith Internet inter-ORB protocol (IIOP), object remoteprocedure call (ORPC), remote method invocation (RMI),or any other distributed protocols. Therefore, Jini hasadvantages of location- and protocol-independent distrib-uted architectures and of dynamic join and exits of serviceswith no impact on the network, in contrast to otherdistributed technologies such as common object requestbroker architecture (CORBA), RMI, and distributedcomponent object model (DCOM) (Bettstetter and Renner,2000).A virtual market with which a client agent initially

contacts can federate with other virtual markets in casethat it does not own any requested services. In the Jiniarchitecture, the federation of Jini LUSs can be easilyimplemented (Waldo, 2001), which is one of the advantages

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JVM

ServiceConnection

Agent

Federation

Agent

Service ListLocation List

Jini LUS

PLUS PA

A

Seller

Agent

Buyer

Agent

Mediator

Agent

Search

Download

Connect

Buyer

Agent

JVM

Service Connection

Agent

Federation

Agent

Service ListLocation List

Jini LUS

PLUS P

Seller

Agent

Buyer

Agent

Mediator

Agent

Download

Request

SCAgent

Jini LUS

Request

Request

Connect

Request

Search

Fig. 5. The schematic diagrams for a client agent’s participation in the

multilateral negotiation system. (a) In the case that the service is in the

current PLUS and (b) in the case that the service is in other PLUS.

S. Park, S.-B. Yang / Engineering Applications of Artificial Intelligence 21 (2008) 633–643638

of the Jini architecture. The federation can be formed invarious topologies in the Jini architecture, and we utilize asimple and robust method of forming the bilateralconnection between two lookup services for the proposednegotiation system. Two lookup services can be linked byregistering the proxy of each lookup service. An arbitraryclient agent on a Jini LUS can contact with the services ofother Jini LUSs in the same federation. The client agentneed not know where the service is. The structure of theproposed multilateral negotiation system aforementionedis shown in Fig. 3.

3.3.2. Procedures of participation in the multilateral

negotiation system

There are two different participation procedures in thenegotiation system proposed in this paper. One is for amediator agent and the other for a client agent. Fig. 4shows the registration procedure of a mediator agent in thenegotiation system. In this figure, a mediator agent, whotreats an item of trading and contacts PLUS PA, registersits proxy through the service connection agent on PA. Theproxy of the mediator agent has the name of the item(product KÞ as a proxy’s attribute and includes theconnection information such as the location (URL) andthe contact point (a port number) of the mediator agent.The service connection agent on PA appends the serviceproperties (such as the name of the item) of the mediatoragent to the service list and registers the proxy of themediator agent in the lookup service on PA.

The buyer agent, who is in the vicinity of PA and wantsto purchase K, requests the service connection agent on PA

to find a mediator agent of trading K. The serviceconnection agent requests the lookup service to providethe proxy of the mediator agent with the client agent incase that the service is on PA as shown in Fig. 5(a). If thereis no service in PA, the service connection agent on PA

recognizes the absent of the service in PA, and requeststhe federation agent on PA to find it (Fig. 5(b)). In thelisted order in the location list, the federation agentrequests the service connection agents on other PLUSs

JVM

ServiceConnection

Agent

FederationAgent

Service ListLocation List

Jini LUS

PLUS PA

Buyer

Agent

Mediator

Agent

Seller

Agent

UpdateRegister

Request

Seller

Agent

Buyer

Agent

Fig. 4. The service registration of a mediator agent in the multilateral

negotiation system.

to find the service. When the service is found in PB, theservice connection agent on PB informs the federationagent on PA of the existence of the service, and then thefederation agent on PA federates with PB. The buyer agent,thereafter, can download the proxy of the mediator agentfrom PB. Note that all the services on a PLUS in afederation are available to all the client agents in the samefederation. The buyer agent finally can connect with themediator agent using the connection information includingthe downloaded proxy. Seller agents also follow the sameprocedures.

3.3.3. Procedure of matching couples in the multilateral

negotiation system

A client group consists of the client agents who haveconnected with a mediator agent. In each round ofnegotiations, a client group is reorganized with the clientswho entered newly for the current round and the clientswho have failed to reach agreements in the previous round.In multilateral trading a mediator agent must determinethe final couple in the client group according to a givenmatching criterion. By the final couple we mean a pair of abuyer and a seller finally matched in a round. The period ofone round is dependent on the properties of the product in

ARTICLE IN PRESSS. Park, S.-B. Yang / Engineering Applications of Artificial Intelligence 21 (2008) 633–643 639

a negotiation. The process of determining the final couplesin a round is described below:

Step 1: Constructing the negotiation partners. A

negotiation partner is a pair of a buyer agent and a selleragent who have the CNRs of all the attributes. Since amediator agent finds all possible negotiation partners in aclient group, a buyer agent can be related with more thanone seller agent and vice versa. Therefore, a buyer or aseller agent may belong to more than one negotiationpartner. The mediator agent evaluates the joint profits ofthe negotiation partners with the formulation aforemen-tioned in Section 3.2.

Step 2: Determining the final couples in the currentround. A mediator agent is designed to find negotiationpartners and determines the final couples among thenegotiation partners according to the matching criterions.In the case of the maximum profit criterion, the mediatoragent evaluates the profits of all the negotiation partnersand sorts them in the non-increasing order of joint profit.Thereafter, the final couples are determined from thesorted list, while removing buyer agents or seller agentswho have already been included in the final couples. Thetime complexity of determining the final couples withthe maximum profit criterion is OðN logNÞ, where N is thenumber of negotiation partners, since sorting takes longertime than any other operations. In the case of themaximum cardinality criterion, a mediator agent gets asmany final couples as possible without concerning theprofits. A mediator agent in this criterion is designed tomatch the couples with the Ford–Fulkerson algorithm thatsolves the maximum cardinality matching in a bipartitegraph. It finds the final couples in O(NM) time, where M isthe number of client agents who participated in thenegotiation (Evans and Minieka, 1992). One of thematching criterions should be predetermined for eachmediator agent according to the characteristics of negotia-tions such as dealing products, prices, and other attributesof a product. The mediator agents prepare the next round

Seller Agent

Seller Agent

Seller Agent

Seller Agent

Seller Agent

Seller Agent

Buyer Agent

Buyer Agent

Buyer Agent

Buyer Agent

Buyer Agent

Newly enteredagents in current

round

Agents who failed

to get matched in

the previous round

Dotted line :Negotiation Partners

Solid Line :Final Couples

Fig. 6. An example of a round in a multilateral negotiation.

with the client agents who have failed to get matched alongwith newly entered client agents. Fig. 6 illustrates anexample of a round in a multilateral negotiation.

4. Experimental environments

4.1. A trading scenario for simulation

In this paper we have chosen used cars for tradingbecause of their appropriateness for representing thevarious propensities to consume, although any commod-ities can be traded in the proposed multilateral negotiationsystem. In the trading model, each buyer wants to purchasea specific model of a used car in a virtual market wheresellers wish to trade various models of used cars. Theremay be more than one seller with the same model in eachround; however, a negotiation can still be carried onbecause each car may have different values for its attributesin general.The scenario for simulations is as follows. A person

wants to buy a used car for CAR_A model. The buyer nowlaunches a buyer agent who requests its service connectionagent on a nearby PLUS to find the mediator for CAR_A.The buyer agent need not know the locations of themediator agent. The service connection agent now providesthe client agent with the proxy of the mediator agent fortrading CAR_A, who has in advance registered on thesame PLUS. Similarly, a seller agent can find the mediatoragent for CAR_A. Now the mediator agent gathers theparticipants for trading of CAR_A during a fixed timeinterval (called the round time), and then begins to make anegotiation for the current round. The mediator agentfinally matches the final couples in the current round by thenegotiation procedure aforementioned in Section 3.The typical attributes of a used car are its price, the year

when it was manufactured, the mileage on its odometer,and the warranty of the car. The sellers and the buyers havetheir own values on each attribute such as the requestvalues, the allowable values, and the weights of theattributes. The agent for one party does not know anynegotiation information about others. Only the mediatoragent receives the negotiation information from themembers of a client group and carries out a multilateralnegotiation in each round. In this paper the negotiationinformation is prepared so that each attribute couldrepresent all possible variations. Table 1 shows a sampleof negotiation information of a buyer.

Table 1

Sample of negotiation information of a buyer

Attributes Request values Allowable values Weights

Price (dollars) 8000 12,500 0.5

Year (year) 2003 1999 0.2

Mileage (miles) 30,000 50,000 0.1

Warranty (months) 24 12 0.2

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Table 3

The data template for generating buyers

Attributes Request values Allowable values

Price (dollars) ½Paw � 6000;Paw � 3000� 22000pPawp32000

Year (year) [2001, 2004] [1997, 2000]

Mileage (1000mile) ½Maw � 70;Maw � 10� 100pMawp150

Warranty (months) [12, 36] [2, 6]

S. Park, S.-B. Yang / Engineering Applications of Artificial Intelligence 21 (2008) 633–643640

In Table 1 a buyer wishes to purchase a car with $8000.The buyer may make a concession to a seller up to $12,500if the seller proposes a contract that is more profitable tothe buyer in the other attributes (mileage, year, orwarranty) than the previous contract. Furthermore, thebuyer wants a car manufactured in 2003, and may alsomake a concession to the seller up to the year 1999 if betteroffers in other attributes are proposed. Similarly, the buyerwants a car with 30,000mile and may purchase a car withup to 50,000mile if there are better offers in otherattributes. The warranty is requested for 24 months fromthe buyer and is also allowed at least 12 months. Theweights of the attributes shown in Table 1 indicate that thebuyer conceives the price as the most important factor inpurchasing a car, the manufacturing year and the warrantyas the next, and the mileage as the least significant factor.A sample of negotiation information for a seller can begiven similarly.

4.2. Generating simulation data sets

The experimental data sets of multilateral negotiationsystems are randomly generated in order to simulate thereal-world trading model. In the experiments, the negotia-tion information of sellers and buyers is randomly createdfrom Tables 2 and 3, respectively. In Table 2, Preq and Mreq

are the request values of price and mileage, respectively,and they are predetermined prior to the allowable values ofprice and mileage. Similarly, in Table 3, Paw and Maw arethe allowable values of price and mileage, respectively, andthese values are chosen prior to the request values of priceand mileage.

4.3. Equipments for evaluating efficiency of a negotiation

system

In our experiments we simplified the structure ofnetworks and devices as shown in Fig. 7 since we focusonly on the efficiency of the multilateral negotiationsystems. Note that buyer agents and seller agents may ormay not belong to the same PLUS with a mediator agentbecause the services on PLUSs in a federation are availableto all the clients in the federation.

Client agents and mediator agents can be distributedover various networks, and they may perform their dutieson various devices. For the convenience of experiments,however, client and the mediator agents are simulated ondesktop computers. Buyer and seller agents are created in

Table 2

The data template for generating sellers

Attributes Request values Allowable values

Price (dollars) 22000pPreqp32000 ½Preq � 6000;Preq � 3000�

Year (year) [1997, 2000] [2001, 2004]

Mileage (1000mile) 100pMreqp150 ½Mreq � 70;Mreq � 10�

Warranty (months) [2, 6] [12, 36]

the thread unit from the buyer agent server (BASv) and the

seller agent server (SASv), respectively. Therefore, theyhave their own negotiation information and accomplishtheir duties independently without having knowledge of thenegotiation information of other client agents. Eachmediator agent created from the mediator agent server

(MASv) can trade a single product and carries out amultilateral negotiation on multi-rounds independently.In each round new client agents participate in the trading

with the client agents who failed to be matched in theprevious round. Note that a buyer agent may be relatedwith more than one seller agent and vice versa asmentioned in Section 3.3.3.

5. Experimental results

In this paper, we compare the bilateral negotiation systempresented by Faratin et al. (2000) with the proposed systemfor bilateral negotiations. In addition, we have also extendedFaratin’s system so that it can perform multilateralnegotiations and call it the multilateral trade-off system(MTOS) because it is based on the trade-off system. Theproposed system is called MPS, because its matchingcriterion is maximum profits. Although the agent systemsbased on the trade-off mechanism are not limited to thelinear evaluation function, EðxiÞ, we compare the negotia-tion results under the same experimental conditions of linearevaluation function used in Faratin et al. (2000) for faircomparisons. In the comparison of the negotiation systems,we focus on the efficiency of a system with respect to jointprofit, execution time, and the capability of extendingtoward multilateral negotiations in a virtual market.

5.1. The negotiation procedure in the previous negotiation

system

A trade-off can be defined informally as the mechanismin which one party lowers its values on some attributes anddemands more on other attributes at the same time.Therefore, a trade-off helps in searching for a contract thatis equally valuable to the current contract, and suchcontract may benefit the other party as well. In a bilateralnegotiation, let X be an offer of one party (agent qÞ to theother party (agent pÞ, and Y be a subsequent offer fromagent p. A trade-off for agent q with respect to Y can bedefined as follows:

tradeoffqðX ;Y Þ ¼ Z, (7)

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JVM

ServiceConnection

Agent

FederationAgent

Service List Location List

Jini LUS

PLUS

MASv

Mediator

Agents BASv

Buyer

Agents

SASv

Seller

Agents

TCP/IP

TCP/IP

Fig. 7. The structure of simplified equipments for evaluating the efficiency of a negotiation system.

Table 4

The results from bilateral negotiation of both systems for the randomly generated 50 negotiation partners

MTOS (10) MTOS (100) MTOS (200) MPS

Average joint profit 1.197 1.206 1.209 1.268

Average execution time for a bilateral negotiation (ms) 38 366 753 4

S. Park, S.-B. Yang / Engineering Applications of Artificial Intelligence 21 (2008) 633–643 641

where q means agent q, which is either a buyer agent or aseller agent, Z is the offer that satisfies Profitsq

ðZÞ ¼

ProfitsqðX Þ and is assumed to be the offer that is the most

similar to Y. Several negotiation systems have beenimplemented with the various strategies of the trade-offmechanism. Among these systems, the strategies using thepersonal negotiation information of other party couldguarantee higher profits for both parties in a negotiation(Faratin et al., 2000). Therefore, when the similarity isevaluated with the information of the other party such asthe weights of attributes, an agreement of the two canachieve higher profits. In a trade-off system, although theweights of the attributes for one party need not be open tothe other party, it must be exposed to the other partyduring negotiation process for much higher joint profits. InMPS, however, such information is exposed only to themediator agent. In the online environment, the concept ofnegotiations with a trusted mediator is often more usefulthan that of direct negotiations with unspecific persons(Limthanmaphon et al., 2000).

5.2. The results of comparison in bilateral negotiations

In this section the experiments for MPS and MTOS havebeen carried out in order to compare the performances ofbilateral negotiations. Table 4 shows the results of bilateralnegotiations with 50 negotiation partners randomly gener-ated from Tables 2 and 3. In MTOS the hill climbing searchhas been utilized for finding possible contracts. Hence, itsexecution time and joint profits vary according to thenumber of sampling points. For MTOS 10, 100, and 200sampling points have been used.

In Table 4 the execution time and the joint profits ofMTOS increase as the number of sampling pointsincreases. MPS achieves more profits than MTOS with5% on average, and the average execution time of MPS isabout nine times faster than MTOS with 10 samplingpoints. The overall performance of MPS is much betterthan MTOS with any number of sampling points.

5.3. The results of comparison in multilateral negotiations

The process of multilateral negotiations and the match-ing criteria of MTOS are established as in MPS. In MTOS,the joint profit of each negotiation partner is determined bybilateral negotiations of the trade-off mechanism. Notethat the network overheads such as network latency, thecost of federations among PLUSs, and the communicationcost between agents and PLUSs are the same for both MPSand MTOS. Therefore, the comparison of the dynamiccapabilities of the negotiation systems was not made in thispaper. In the simulation the clients who failed to bematched in the previous round and newly generated clientsparticipated in multilateral negotiations for 10 rounds.Note that the numbers of clients who fail to be matched ineach round are different for MPS and MTOS, although thenumbers of newly generated clients in a round are the samefor both systems. The buyers and sellers are newlygenerated with half of the number of clients in Table 5,respectively, except for 30 clients. In case of 30 clients, thenumbers of buyers and sellers are 20 and 10 (or 10 and 20),respectively, as shown in superscripts in Table 5. The

elapsed rounds mean the number of rounds needed for aclient to reach an agreement. For example, average elapsed

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Table 5

The results of both systems according to the number of clients newly entering in each round (during 10 rounds)

Number of clients (generated in each round) 10 20 30a 30b 60

Average joint profits MPS 1.327 1.346 1.442 1.438 1.394

MTOS (200) 1.306 1.272 1.416 1.412 1.373

Average elapsed rounds MPS 1.18 1.15 1.30 1.28 1.15

MTOS (200) 1.19 1.23 1.28 1.38 1.19

aTwenty buyers and 10 sellers.bTen buyers and 20 sellers.

0

10

10 20 30 40 50 60 70 80 90

20

30

40

50

60

70

80

0 100

Rounds

Exe

cu

tio

n t

ime

(s)

MTOS (200)

MTOS (100)

MTOS (10)

MPS

Fig. 8. The average execution times of MPS and MTOS.

S. Park, S.-B. Yang / Engineering Applications of Artificial Intelligence 21 (2008) 633–643642

rounds 1.30 means that the final couples have reached theiragreements after 1.30 rounds on average. It is natural thatin some cases the average elapsed rounds of MPS may beslightly higher than those of MTOS, since each negotiationsystem may have different matched couples in each round.The results of joint profits show that MPS achieves moreprofits (with 2.4% on average) than MTOS regardless ofthe number of clients.

The curves in Fig. 8 show the traces of the averageexecution time every 10 rounds for MPS and MTOS. Inthis simulation 10 clients (five buyers and five sellers) whonewly entered in each round and the clients who failed tobe matched in the previous round participated in negotia-tions during 100 rounds. We considered the execution timeneeded for only multilateral negotiations without consider-ing the network latency. As the rounds are going on, theexecution time of MTOS becomes slow and differs greatlyfrom that of MPS. In MTOS, moreover, the execution timevaries severely in each round due to the nature of theheuristic approach in determining the joint profit ofnegotiation partners. MPS performs a round about 10times faster on average than MTOS (10) over 100 rounds.

6. Conclusions and future work

In this paper, we have proposed an automated negotia-tion system that can efficiently carry out multilateralnegotiations with multi-attributes in pervasive computingenvironments. In order to achieve the applicability towardmultilateral negotiations, the concept of a mediator agentand the bilateral negotiation scheme based on linearprogramming has been proposed. For the proposed multi-lateral negotiation system, we have also introduced aframework of a pervasive environment. In this frameworkthe components can dynamically join/disjoin a virtualmarket which may make a federation with other virtualmarkets. The framework supports efficiently a multilateralnegotiation model in pervasive computing environments.For experiments the trade-off negotiation system has

been implemented and compared with our system for theefficiencies of negotiations under the same experimentalcondition of linear evaluation function. The systemproposed in this paper produces higher profits in negotia-tions and is much faster in execution time than thetrade-off system under the condition of agreement for

ARTICLE IN PRESSS. Park, S.-B. Yang / Engineering Applications of Artificial Intelligence 21 (2008) 633–643 643

reciprocity. Although our system had been implemented byassuming the linearity of evaluation function, our systemimproved the efficiency of the joint profits and theexecution time with the extension toward multilateralnegotiations in the pervasive computing environments.

The issues in relation to the confidence of mediator agentsand the development of delicate protocols for agentinteroperability will be included in the future work. Inaddition, we plan to proceed with more elaborate modifica-tions of the framework for application to practical systems ofthe pervasive computing environments, such as u-hospital.

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Sanghyun Park received the B.S. and M.S. degrees in Mechanical

Engineering from Yonsei University, Seoul, Republic of Korea, in 1996

and 2001, respectively. During 1996–1999, he stayed in Aerospace

Division, Korean Air, and worked as network and system administrator

for Computer Aided Design/Engineering. He is currently a Ph.D.

candidate at the Department of Computer Science, Yonsei University,

and his research interests include e-commerce, intelligent mobile agent,

and ubiquitous network.

Sung-Bong Yang serves as a faculty member at the Department of

Computer Science, Yonsei University, Seoul, Republic of Korea, and his

research interests include e-commerce, mobile system, peer-to-peer

computing, and 3D computer graphics. He received the Ph.D. degree in

Computer Science from the University of Oklahoma, USA, in 1992.