Information Sciences 282 (2014) 1–14

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Information Sciences

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A hybrid multi-agent negotiation protocol supporting agentmobility in virtual enterprises

http://dx.doi.org/10.1016/j.ins.2014.06.0210020-0255/� 2014 Elsevier Inc. All rights reserved.

⇑ Corresponding author. Address: Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam RoKong. Tel.: +852 2859 7055; fax: +852 2858 6535.

E-mail addresses: wanggong@connect.hku.hk (G. Wang), tnwong@hku.hk (T.N. Wong), arielxhwang@gmail.com (X. Wang).

Gong Wang, T.N. Wong ⇑, Xiaohuan WangDepartment of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong

a r t i c l e i n f o

Article history:Received 1 March 2011Received in revised form 15 April 2014Accepted 11 June 2014Available online 19 June 2014

Keywords:Negotiation protocolMulti-agent systemMobile agentVirtual enterpriseOntology

a b s t r a c t

It is a growing trend for supply chain members to collaborate in a virtual enterprise (VE). AsVE members are usually geographically dispersed and they have to cooperate and negoti-ate to achieve mutual agreements, the distributive multi-agent systems (MAS) are beingincreasingly adopted for VE implementation. To cope with the flexibility requirements ofVE, a new MAS architecture is proposed in this paper. In this MAS, the VE initiator canbe represented by either stationary or mobile agents to negotiate with VE partners. Asthere is not a mobile agent-based negotiation scheme considering both the negotiationeffectiveness and system security, a hybrid multi-agent negotiation protocol is establishedin this paper to tackle this problem. This hybrid protocol incorporates both the stationaryand mobile agent negotiation phases to compose a more efficient and successful multilat-eral agent interaction regulation. In this negotiation process, mobile agent-based negotia-tion is first initiated, it will then switch to stationary agent-based negotiation when mobileagent migration is rejected or interaction is blocked in the remote host. Besides, the ontol-ogy-based approach is adopted in the MAS and an embedding ontology operation protocolis established to refine the knowledge expression pattern in the agent negotiation process.The validity and efficiency of the protocol are verified through the execution of a hypothet-ical VE negotiation case.

� 2014 Elsevier Inc. All rights reserved.

1. Introduction

To compete in today’s prevailing open market environment, enterprises have to improve the overall performance andeffectiveness of products and services. It would be beneficial for a company to collaborate with its supply chain partnersto establish a flexible strategic alliance featured as a virtual enterprise (VE). Here, VE can be defined as a way of organizingmanufacturing and supply chain activities, where different and independent partners exploit business opportunities byestablishing an enterprise cooperative [5]. Formed by autonomous and geographically distributed partners, the VE is distrib-utive in nature and VE partners need to negotiate frequently to seek a mutually agreeable solution for resource and serviceallocation.

The cooperation of VE is supported by computer networks. While the rapid development of e-commerce promotes theinteraction and transaction activities between VE partners, negotiations between VE members may have to be conductedin an automated manner. In recent years, the multi-agent technology has been used to model the negotiation process in

ad, Hong

2 G. Wang et al. / Information Sciences 282 (2014) 1–14

business and supply chain management applications. Relying on a distributed agent platform, intelligent software agents canperform negotiations representing their owners. In this regard, mobile agents with the ability of moving from host to host toexecute local interactions can be employed, because the local interaction is considered to be more efficient than the remoteone [35]. The main concern of using mobile agents is security. Unlike the open e-commerce environment, the VE environ-ment is a semi-open system toward familiar partners with long-term or short-term relationships other than the one-shottransaction relationships often appear in the e-commerce environment. A semi-open system is often built upon a commonsoftware platform or platforms with controlled transparency. This arrangement will facilitate the control of mobile agentsand simplify their travelling routes. Besides, the mutual trust built up through VE partners’ constant contacts can make itrelatively secure for agents to migrate among those familiar hosts.

To ensure both the system security and effectiveness is a challenge to explore the application potential of mobile agent-assisted automated negotiations. It is essential to specifically design a negotiation protocol to regulate agents’ movementswith security concerns, otherwise, agents may not migrate to the right position, and both the mobile agents and hosts wouldbe at an increased risk of intrusion and attack by the other side. All these occurrences will make the negotiation unsuccessful.Also, it is not easy to equip the negotiation knowledge to agents and make the interaction messages mutually understand-able. To deal with these issues, this paper proposes a hybrid multi-agent negotiation protocol to regulate the mobileagent-assisted negotiation, and embeds an ontology operation protocol in it to govern negotiation knowledge. The hybridprotocol is designed to support both the mobile and stationary agent negotiations for the consideration of system efficiency,robustness and security. Particularly, organization of the negotiation knowledge is based on the concept of ontology, and anontology operation protocol is embedded to ensure ontology interoperability between negotiating agents.

The rest of this paper is organized as follows. In Section 2, the related research background is discussed. In Section 3, amulti-agent system (MAS) architecture is established for VEs. Section 4 elaborates the hybrid multi-agent negotiationprotocol regarding the hybrid protocol expression, the agent decision function and the knowledge organization method.Section 5 implements the protocol and gives the results of some comparison experiments. In Section 6, related works arereviewed concerning the methods and technologies involved in this paper. Finally, conclusions are drawn in Section 7.

2. Background

Stemming from artificial intelligence (AI), an agent can be perceived as a software computational entity possessing theproperties of autonomy, social ability, reactivity and pro-activeness [45]. A multi-agent system (MAS) is a loosely couplednetwork of agents that interact to solve problems which are beyond the individual capacities or knowledge. The MAS meth-odology is especially suitable for modeling the VE systems because of its distributive problem solving ability. It provides apromising approach to both VE architecture modeling and VE collaboration modeling. Under the MAS paradigm, a VE can beportrayed as a set of intelligent agents belonging to different VE partners, each being responsible for one or more activitiesand interacting with other agents to accomplish the planning, scheduling and transaction tasks.

In a MAS, agents can either be mobile and static. Mobile agents are capable of transporting their execution betweenmachines on a network, while static agents can only reside in one location. Especially in distributed systems, mobile agentscan perform multiple interactions with software residents on a remote computer in order to have a complex and closecommunication or the required planning [31]. The beneficial aspects of mobile agents mainly involve the asynchronousautonomous interaction, the reduction of network traffic and latency [12], easy tracking of the involved computers, and car-rying the tasks to wherever they are needed [4]. Two important issues have to be considered when utilizing mobile agents ina MAS: the regulation of agent movement, and the security for both hosts and agents.

Agents’ cooperative problem-solving abilities in MASs can only be attained through comprehensive interactions.Negotiation is an effective agent interaction mechanism enabling autonomous bidirectional deliberation in both situationsof competition and cooperation. To build a sophisticated agent negotiation model, three broad areas have to be considered[14,23]: negotiation protocols governing the rules of interaction, negotiation objects containing the range of issues on whichagreements must be achieved, and negotiation decision making models guiding agents’ concession behaviors. The ContractNet Protocol (CNP) [39] which specifies the problem-solving communication in the form of contract negotiation is widelyemployed in agent negotiation models. It provides the fundamental negotiation regulation by the process of mutual selectionbased on a two-way exchange of information between a manager and a contractor. The CNP may need to be extended ormodified to accommodate negotiations involving mobile agents in the VE environment.

In the agent-based negotiation system, all the negotiation subjects and negotiation behaviors have to be clearly expressedto facilitate agents’ autonomous actions. To represent the knowledge and rules hierarchically, the concept of ontology hasbeen adopted in recent researches on automated negotiations [3,30,41]. An ontology provides a vocabulary for representingknowledge about some topic and a set of relationships and properties that hold for the vocabulary entities. Ontologies areunderstood as means to share and reuse knowledge [38] in which domain knowledge is strictly separated from softwareimplementations and can thus be efficiently reused across heterogeneous software platforms [20]. For a community ofagents, the ontology interoperability must be tackled to guarantee the mutual understanding. On the other hand, the knowl-edge organization method should better not add extra burden to the serialization and unserialization processes during agentmigration [1].Considering these requirements, the ontological knowledge organization method still has to be explored andrefined.

G. Wang et al. / Information Sciences 282 (2014) 1–14 3

3. The proposed multi-agent architecture for VE

According to the specific business opportunities and objectives, a VE initiator has to identify and select appropriate part-ner companies to join the VE. Typically, a VE may comprise long-term and ad-hoc collaborators. To cope with the flexibilityrequirements in the VE configuration, a new MAS architecture is proposed in this paper. Most of agent-based systemsemploy only stationary agents whereby agent interactions involve two agents residing in different servers. For those MASwith mobile agent implementations, mobile agents can be sent one time to a remote server to interact with a stationaryagent. The novelty of the proposed MAS is that a hybrid negotiation protocol is established to support agent interactionsinvolving either stationary or mobile agents with flexible switching.

This can be illustrated with a simple hypothetical example. A manufacturing company X is going to establish a VE withcollaborating companies to fulfill the manufacturing requirements of a product P. Specifically, X will focus on main assemblyoperations and collaborators will be involved in the production of sub-assemblies S1 and S2. Suppose 3 companies A, B and Care potential collaborators. Among them, A is the only supplier of S1 and both B and C can supply S2. Regarding partner rela-tionship, A is a new collaborator; B is a short-term VE participant whose performance so far is unstable; C is a long-term VEpartner with whom X has built reliable business relationship. In accordance with the FIPA (The Foundation for IntelligentPhysical Agents) agent platform reference model [17], the proposed MAS architecture for this example is established, seeFig. 1. Agent containers are established in the local servers of the VE participants (X, A, B and C). Different types of agentshave been defined, namely, Task Decomposer Agent (TDA), Coordinator Agent (CA), Buyer Agent (BA) and Seller Agent(SA). The proposed MAS architecture also comprises the logical components of Directory Facilitator (DF), Agent ManagementSystem (AMS) and Message Transport Service (MTS). The DF provides yellow pages services to other agents. Agents may reg-ister their services or query other services within the DF. The AMS exerts supervisory control over access to and use of theagent platform. It maintains a directory of agent identifiers (AID) containing transport addresses for agents registered. TheMTS controls all the exchange of messages within the platform.

In the MAS, Buyer Agent (BA) and Seller Agent (SA) are designed to represent the buyer and the seller, respectively, tonegotiate on the acquisition and provision of products and services in a typical supply chain. In this example, the VE initiatorX acts as a buyer to acquire the sub-assemblies S1 and S2 from VE partners A, B and C. X is therefore represented by BAs tonegotiate with the respective SAs of the three partners. Besides, the VE initiator is equipped with a Task Decomposer Agent(TDA) and a Coordinator Agent (CA). The TDA decomposes VE operational tasks including planning, manufacturing andoutsourcing into specific executable sub-tasks. A CA is then responsible for a particular sub-task decomposed by the TDA.To accomplish a sub-task, the CA initiates and governs several BAs to negotiate with SAs of the related sellers. It designatesnegotiation knowledge and provides consultation to the BAs during the negotiation process, and decides when to changenegotiation strategies and which seller wins the contract comparing the negotiation results gathered from each BA–SA nego-tiation pair. An important feature in this MAS architecture is that a BA can either be a stationary agent or a mobile agent. Ifsituation permits (e.g. for long-term business partners with trust), mobile agents can be employed to travel to remote hoststo negotiate. However, when it is not suitable to send a mobile agent to the remote host of a new or occasional collaborator, astationary BA has to negotiate with a corresponding remote SA across the network. As shown in Fig. 1, a mobile BA is sent tothe host of C while stationary BAs are employed to deal with A and B.

4. The hybrid negotiation protocol

Depending on the number of interaction partners and the relationship between the VE initiator and individual VE collab-orators, the following types of agent interactions may exist in the MAS: one-to-one agent interaction, one-to-many agent

VE Initiator (X)

VE Partner1 (A)

<Host 3>

Container-2SA1

Main-Container

VE Partner2 (B)

<Host 4>

Container-3

VE Partner3 (C)

<Host 5>

Container-4SA3

DF

DistributedAgent

PlatformAMS

Database Database Database

BA3

Stationary agent Mobile agent

Message Transport System

SA2

<Host 1>

move

connect

connect

New participant Short-term participan t Long-term participant

Leading participant and organizer

Database

RulesEngine

<Host 2>

TDA

BA1

CA1 CA2

BA2

Container-1

Fig. 1. The architecture of distributed VE agent platform.

4 G. Wang et al. / Information Sciences 282 (2014) 1–14

interaction, stationary agent-stationary agent interaction, and mobile agent-stationary agent interaction. A hybrid multi-agent negotiation protocol is proposed to provide a successful set of interaction and functioning rules to support negotiationsin VEs.

4.1. Protocol expression

Fig. 2 depicts the two cases of one-to-one buyer–seller negotiation in the MAS, where the buyer agent (BA) can either be astationary agent or a mobile agent. Here, the only difference is that the mobile BA instance is sent to the seller agent (SA)’shost to negotiate and then moves back to its original host when the negotiation result is generated.

Regarding the one-buyer-many-seller negotiation pattern, the buyer is represented by multiple BA instances initializedby the CA to negotiate with respective sellers represented by SAs. Thus, the one-to-many negotiation pattern will be orga-nized as concurrent one-to-one negotiations. Through iterative negotiation concessions, each pair of BA instance and SA willgenerate a negotiation result. The CA then collects all the negotiation results and selects the preferable sellers. This approachwas originally used to govern the stationary agent negotiations as presented in [43,44]. It is still applicable when mobileagents are employed, in this circumstance, the CA can control the stationary and mobile BA instances concurrently. Thisis illustrated in the three situations in Fig. 3: pure stationary agent negotiations, pure mobile agent negotiations and mixedstationary and mobile agent negotiations. CA’s final selection function will not be influenced by the BA’s existing state (sta-tionary or mobile), since all BA instances have to send back negotiation results for selection. Hence, the hybrid negotiationprotocol comprises five negotiation scenarios described in Figs. 2 and 3.

Since the one-to-many negotiation can be converted to concurrent one-to-one negotiations, the following discussion onhybrid phases of the negotiation protocol will be simplified between a BA instance and a SA. Fig. 4 represents the hybridnegotiation protocol using AUML (Agent Unified Modeling Language) in line with the FIPA interaction protocol specification[16]. Four agent roles are displayed in the AUML diagram. The expression such as BAk/Buyer:Agent denotes a distinguishedagent instance BAk satisfying the agent role Buyer belonging to the class Agent.

As depicted in the AUML diagram (Fig. 4), two sub-protocols HP_M (Hybrid Protocol_Mobile) and HP_S (Hybrid Proto-col_Stationary) are defined. The HP_M protocol demonstrates the mobile negotiation sub-protocol in which the buyeragent’s life line moves from Host 1 to Host 2 to negotiate locally with Sellerk, while the HP_S protocol describes the station-ary negotiation sub-protocol in which the buyer agent keeps residing in Host 1 to carry out negotiation remotely with Sellerkin Host 2.

For each of the sub-protocols, the message exchange sequences for negotiation concessions are the same, which take theform of alternate and iterative exchanges of ‘‘call for proposal (cfp)’’ and ‘‘propose’’ messages. The message sequencesexchanged between agents are complied with the FIPA ACL (Agent Communications Language) message structure and theFIPA Communicative Act Library [15].

4.2. Agent decision function

When a message is received, the agent will respond by executing some decision functions which are self-conductedactions (represented by rectangles with turn-back arrows, such as Evaluate, Check1, etc.) along the agent life line inFig. 4. Concerning the switching decision function between the HP_M and HP_S sub-protocols, it is mainly included in theCheck actions as denoted in Fig. 5.

Check 1: Before a mobile buyer agent moves to the seller’s host, it must request the AMS for the seller agent’s locationinformation. According to the interaction history, if the seller’s location host is malicious for mobile agents, the buyer agentwill decide to execute HP_S. If the location is safe, the buyer agent will send the request_arrival message asking the seller’spermission of migration. If it is a new seller agent without any former interaction, the sub-protocol switching decision has tobe made considering the network utilization rate and the CPU usage rate of the buyer agent’s location host.

Check 2: For the seller agent, in response to the buyer’s request_arrival message, it will check the buyer agent’s AID (agentidentifier) which contains the information of the mobile agent’s name and address. If the AID belongs to the list of secure

NETWORK

BA SA

CA

NETWORK

CA

SABABA

Control Message Move

Stationary negotiation Mobile negotiation

1-Negotiate

2-Informresults

1-Move 2-Negotiate

3-Move back

4-Informresults

Fig. 2. One-to-one negotiation scenarios.

NETWORK

SA

CA

BA

BA SA

SABA

NETWORK

CA

SABA

SABA

SABA

NETWORK

CA

SABA

SABA

SABA

BA

BA

BA

BA

BA

Control Message Move

Parallel stationary negotiation Parallel mobile negotiation Parallel stationary and mobile negotiation

Fig. 3. One-to-many negotiation scenarios.

Fig. 4. The AUML expression of the hybrid negotiation protocol.

G. Wang et al. / Information Sciences 282 (2014) 1–14 5

agents, the immigration will be allowed by sending the agree_arrival message and the protocol will switch to HP_M. Other-wise, the accessing request will be rejected by sending the refuse_arrival message and the protocol will switch to HP_S.

Check 3: After the buyer agent moves to the seller’s host, it will continuously check if there is any sudden change of theseller’s concession pattern or interaction blocking. These evidences may be caused by the seller agent’s attempt of eaves-dropping or tampering the buyer agent’s negotiation behavior, which can be seen as attack hints. If any attack hint iscaptured, the buyer agent will cease the HP_M and move back to its original host to resume the HP_S for the following nego-tiation process.

Through these three Check actions, the sub-protocol switching decision function considers security issues during agentmigration and enhances the failure tolerance ability when agent mobility is interrupted at some point of execution. For amobile agent, through Check 1 and Check 2 decision functions, it has a probability of 75% to keep stationary toward a familiarhost, and a probability of 62.5% to keep stationary toward an unfamiliar host. That is, theoretically, the stationary agentnegotiation is more prone to happen considering the safety issues. Check 1 and Check 2 can be implemented prior to the

<HP_S> <HP_M>Message:inform_location

Locationfamiliarity

malicious safe

Networkutilization<50%&&CPU

usage<50%?

unfamiliar

yes

noMessage: request_arrival

Arrivalagreed?

no

Attackcaptured?

yes

yes

no

Che

ck 1

Che

ck 2

Che

ck 3

Fig. 5. Switching decision between HP_M and HP_S.

6 G. Wang et al. / Information Sciences 282 (2014) 1–14

exchange of negotiation proposals by searching within an interaction performance database. Check 3 can be implementedwhen a mobile agent has moved to a remote host. The mobile agent traces the opponent’s concession steps to detect suddenfluctuation and sends back status messages to the original host for safety verification, if the original host cannot receivestatus message on time, it will call back the mobile agent or withdraw the negotiation thread.

4.3. The negotiation knowledge organization method

In an automated negotiation system, agents have to convey all their negotiation considerations through iterativeexchanges of messages. Especially in the VE environment, a wide range of negotiation knowledge may be involved relatingto the description of products, pricing, transportation, service, and so on. Hence, a highly structuralized knowledge organi-zation method is in need. Furthermore, for a negotiation protocol supporting agent mobility, the knowledge organizationmethod should better not add extra burden to the serialization and deserialization processes when sending the mobileagent’s codes and state through the network.

In this regard, ontology is a more feasible knowledge organization method. Firstly, ontologies define the hierarchicalknowledge architecture and the relationships between terminologies which cannot be clarified by controlled vocabularies[13]. A controlled vocabulary is a list of terms that have been enumerated explicitly and it only has a small selection of rulesand constraints, while an ontology has more rules to infer class membership based on a subclass/superclass hierarchy.Secondly, ontologies separate the domain knowledge from software implementations [21], and then the knowledge struc-ture does not need to be hard-coded within software agents so as to alleviate the migration burden. Thirdly, ontologies pro-vide the basis for automated reasoning which will be beneficial for agent intelligence development.

In this paper, the ontology is structured as concepts and agent actions to express the negotiation objects and negotiationbehaviors. Fig. 6 gives an example of how the negotiation knowledge is organized. Here, concepts are entities describing therelevant elements or terms in the negotiation domain. For instance, the Issue concept indicates the description of a nego-tiation item through three attributes, more specifically, when price is a negotiation issue, the issue name is ‘‘Price’’, theissue value might be ‘‘100’’ and it can be measured in ‘‘HK dollar’’. Agent actions are special concepts indicating whatactions agents can perform when dealing with other concepts. For instance, the Order agent action conveys that an agentmay order some products from a seller considering items such as price and lead time by the end of some deadline. Both the

-issueName: String-issueValue: String-inMeasure: String

Issue -hasOrderID: String-hasProduct: Instance-hasIssue: String-hasDeadline: String

Order-hasOrderID: String-productDes: String-toSeller: Instance-hasItem: String-hasDeadline: String

Order -hasItemName: String-forNegotiation: String-hasItemValue: String-hasItemMeasure: String

ItemAgentAction AgentAction

-forItem: Instance-forParticipant: Instance-hasConcessionTrend: String-hasConcessionConvexity: String-hasConcessionStep: Float

Strategy

-hasItem: Instance-fromParticipant: Instance-hasFluctuation: String-hasDetection: Float

ItemStatus

Concept

-hasAgentName: String-hasAddresses: String-hasResolvers: Instance

AID

hasResolvers*

forItem

hasItem

forParticipant

fromParticipant

toSeller

Shared ontology matching list

<SellerNegotiationOntology> <BuyerNegotiationOntology> <BuyerPrivateOntology>

Seller Host

Concept Concept

Buyer Host

<SellerPrivateOntology>

Concept AgentAction

Fig. 6. The organization of negotiation knowledge via ontologies.

G. Wang et al. / Information Sciences 282 (2014) 1–14 7

concepts and agent actions are characterized by a set of attributes. Messages exchanged between agents are composed ofagent actions and related concepts.

Being autonomous entities, VE participants always build their own ontology architectures. Hence, to ensure mutualunderstanding between agents utilizing different ontology structures, a set of shared negotiation terms must be spreadbetween the negotiating agents ahead of the negotiation. As presented in Fig. 6, the ontology is separated into two parts.Firstly, the shared negotiation ontologies are transparent between negotiating agents through a matching list so as to pro-vide mutual understandable negotiation terms. Secondly, the private ontology keeps the negotiation strategic knowledgewhich guides the agent’s concession behavior and not to be uncovered to the negotiation opponent (except the termsinvolved in both the shared negotiation ontology and private ontology).

An essential component in the hybrid negotiation protocol is the embedded ontology operation protocol dealing with theontology interoperability when negotiating agents send messages utilizing their own ontology structures. This embeddedprotocol is depicted in Fig. 7. The shared buyer and seller negotiation ontologies have to be packaged in both the buyer’sand seller’s hosts, and their ontology matching list has to be coded within buyer and seller agents. During migration, themobile agent only brings the initial individual values of the shared ontology and private ontology instead of the wholeset of ontology structure. Considering the ontology structure in Fig. 6, an example of an individual value of the term Item

in the shared ontology is ‘‘hasItemName Price, forNegotiation yes, hasItemValue 100, hasItemMeasure HKD’’.Ontologies must be registered beforehand within agents to ensure the utilization. Hence, after the buyer agent moves tothe seller’s host, it should immediately re-register the access of shared buyer and seller negotiation ontologies so as to utilizethose ontologies again.

In the ontology operation protocol, the message sent by the mobile buyer agent relies on the buyer negotiation ontology.When the seller receives this message, it translates the message content into the form of seller negotiation ontology accord-ing to the ontology matching list. For a simple example in Fig. 6, the Issue concept in the seller negotiation ontology and theItem concept in the buyer negotiation ontology are matching terms in the ontology matching list, then when the sellerreceives the BNO (buyer negotiation ontology) message in the format of (. . . :hasItemName Price :forNegotiation

yes :hasItemValue 100 :hasItemMeasure HKD. . .), it will translate the message into the SNO (seller negotiation ontology)format as (. . . :issueName Price :issueValue 100 :inMeasure HKD. . .). Through translation, the buyer agent’s messagecan be understood and subsequently evaluated by the seller agent. This process also works when the buyer agent receivesmessages from the seller agent. When the mobile buyer agent needs to change the values of its private ontology, it has tomove back to its original host to consult the coordinator agent to make the changing decision. This arrangement keepsthe processing of sensitive negotiation strategy knowledge within the buyer host and the messages containing negotiation

Coordinator:Agent

BAk/Buyer:Agent

Sellerk:Agent

Buyer Host Seller HostBuyerNegotiationOntology (BNO)SellerNegotiationOntology (SNO)

Shared ontology matching list

RegisterBPO BNO

Register BPO BNO SNO

BNOmessage Translate

to SNO

Re-registerBNO SNO

EvaluateSNOmessageTranslate

to BNO

Evaluate

BPOmessage

Re-register BPO BNO SNOBPO

messageBPO

messageChange

BuyerPrivateOntology

(BPO)

SellerPrivateOntology

(SPO)

Fig. 7. The embedded ontology operation protocol.

8 G. Wang et al. / Information Sciences 282 (2014) 1–14

strategy knowledge will never reach the seller host, so the protocol can avoid the leaking of the private negotiationknowledge.

5. Experiments and results

JADE (Java Agent Development Framework) is selected as the test bed for the proposed protocol because it is a distributedagent platform supporting both agent mobility and ontologies [2]. In the following part, the proposed protocol is imple-mented based on the hypothetical example in Section 3. Some comparison experiments are done to evaluate the efficiencyof the protocol.

5.1. Implementing the negotiation protocol

For VE initiator X and collaborators A, B and C, their agents and containers are summarized in Table 1. Each agent instancehas a unique local name spreading the whole VE agent platform. CA1 governs BA11 to negotiate with SA in a one-to-onenegotiation manner, while CA2 governs two instances (BA21 and BA22) of a BA to negotiate with SA2 and SA3 in a one-to-many manner.

To evaluate the negotiation proposals, the multi-attribute utility theory (MAUT) [26] is employed. It is a tool for makingdecisions involving multiple interdependent objectives based on uncertainty and preference analysis. By rescaling a numer-ical value (utility) on the negotiation issues into a 0–1 scale, it allows the direct comparison of proposals comprising multiplenegotiation issues. Another reason for adopting MAUT is that, comparing with other complicated evaluation methods, MAUTis straightforward to be coded and efficient to be executed in agents through computing. In this paper, the main concern oncomposing the utility function is to rescale the numerical value into a 0–1 scale reflecting the interests of negotiators.Besides the common linear utility functions, non-linear utility functions can be used to specify different attitudes towardrisk. The non-linear functions presented in [14] are adoptable, since they have the potential to compose a continuous spec-trum of utility functions by adjusting the parameters so as to reflect various attitudes of decision makers. The generic expres-sion of the MAUT-based utility function in this paper is given in Table 2. The parameter b controls the attitude toward risk,that is, the smaller the b value is, the more prone the decision maker prefers to risk the loss of negotiation convergence toachieve higher utility. The aggregated utility of a proposal is the weighted accumulation of all the negotiation issues. Whenthe aggregated utility of a proposal is no less than the minimum utility an agent can bear, the proposal would be acceptablefor that agent.

In this paper, the negotiation procedure is perceived as tradeoffs between negotiation issues, although the aggregationmethod may cause the compensation problem (the possibility of offsetting a disadvantage on some negotiation issue by asufficiently large advantage on another issue), the non-linear utility functions may mitigate the effect of the compensabilityby adjusting the rate of utility change through parameter setting. That is, a sufficiently large advantage on issue one will notoffset a disadvantage on issue two when the utility of issue one is calculated to be a smaller number by setting an appro-priate b value in the non-linear utility function. For further considerations, if no compensability is allowed in the negotiation,the negotiation issues can be negotiated separately, or evaluated by using if. . .then. . . rules for regulation.

For experiment parameter settings, the procurement amount for S1 and S2 are 10,000 and 40,000 respectively, and theyare fixed. Unit price and delivery due date are two negotiation issues, and their acceptable ranges are indicated in Table 3.The concession method is simply put as conceding from the desired value to the reservation value through a limited conces-sion steps. The negotiation time limit is set as 4000 ms (ms). BA21 and BA22 are with the same negotiation parameter set-tings since they are instantiated from the same BA. Linear and non-linear utility functions are used to differentiate agents’attitudes when computing the utilities of cfps and proposals. When non-linear polynomial functions are used (as thefunction for price of SA2), an agent holds a patient attitude toward the negotiation issue, and only gives higher utilitiesfor those issue values which are close to its desired value. When non-linear exponential functions are used (as the functionfor due date of SA2), an agent has a desperate attitude toward the negotiation issue, and may give higher utilities as long asthey exceed its reservation value. When linear utility functions are used (as for BA and SA1), none of the above preferences isrelevant to an agent, and the agent has an indifferent attitude.

For the agent migration condition settings, SA1 will reject BA11’s moving request because migrations of mobile agents areforbidden in A’s host. SA2 permits the immigration of BA21, but it has unstable negotiation behavior which will suddenlyblock the on-going negotiation. SA3 has reliable negotiation behavior; however, the network condition between SA3 andBA22 is not so good because SA3’s host is utilizing wireless connection and the bandwidth is a bit low.

Table 1Agents and containers summarization.

X A B C

Container name Main-Container Container-1 Container-2 Container-3 Container-4Agent type DF, AMS Task Decomposer, Coordinator, Buyer Seller Seller SellerAgent name df, ams TDA1,CA1,CA2,BA11, BA21, BA22 SA1 SA2 SA3

Table 2The expression and explanation of utility functions.

Expression Notation

Utility function (polynomialand linear) b 6 1 VðItðiÞÞ ¼

Vimin þ ð1� Vi

minÞ �It ðiÞ�mini

maxi�mini

� �1=bBenefit

Vimin þ ð1� Vi

minÞ �maxi�It ðiÞmaxi�mini

� �1=bCost

8><>:

V(It(i)): utility function for negotiation issue i in the tthnegotiation roundIt(i): the value of negotiation issue i in the tth negotiationroundmini: the minimum acceptable value of negotiation issue imaxi: the maximum acceptable value of negotiation issue i

Utility function (exponential)b > 1 VðItðiÞÞ ¼

exp 1� It ðiÞ�mini

maxi�mini

� �bln Vi

min

� �Benefit

exp 1� maxi�It ðiÞmaxi�mini

� �bln Vi

min

� �Cost

8>><>>:

Vimin: the minimum utility determined by decision makers

b: a positive real number which can reflect the riskpreference level of the decision maker

Aggregated utility UðPðskÞtÞ ¼P

iwðbkÞi � VðItðiÞÞ U(P(sk)t): Buyer Agent’s utility on the Seller Agent’sproposal in the tth negotiation roundP

iwðbkÞi ¼ 1� �

w(bk)i: Buyer Agent’s weighting of negotiation issue i

Table 3The negotiation parameter settings.

Acceptable price Acceptable due date Step limit Utility function

BA11 [90,150] [1,7] 60 0:8� 0:1þ 0:9� 180�Price180�100

� �þ 0:2� 0:1þ 0:9� 7�Duedate

7�1

� �BA21&BA22 [100,180] [1,7] 60 0:8� 0:1þ 0:9� 150�Price

150�90

� �þ 0:2� 0:1þ 0:9� 7�Duedate

7�1

� �SA1 [100,175] [2,9] 50 0:85� 0:18þ 0:82� Price�100

175�100

� �þ 0:15� 0:18þ 0:82� Duedate�2

9�2

� �SA2 [90,210] [4,12] 95 0:75� 0:08þ 0:92� Price�90

210�90

� 4h i

þ 0:25� exp 1� Duedate�412�4

� 2 � ln 0:08h i

SA3 [110,200] [3,6] 70 0:72� 0:1þ 0:9� Price�100175�100

� 2h i

þ 0:28� 0:1þ 0:9� Duedate�36�3

� �

Table 4Negotiation results for different protocols.

One-to-one negotiation One-to-many negotiation

BA11 M SA1 BA21 M SA2 BA22 M SA3

Mobile protocol (1) Request arrival at NRd = 3 (4) Interaction block at NRd = 38 Agreement at NRd = 75Refuse arrival at NRd = 4 Interaction stop at NRd = 38 Time consumed = 2640 msInteraction stop at NRd = 5 No negotiation result Price = 149.3, Duedate = 5No negotiation result BU = 0.445, SU = 0.367

Select SA3

Stationary protocol (2) Agreement at NRd = 69 (5) Agreement at NRd = 94 Out of time at NRd = 72Time consumed = 1765 ms Time consumed = 3156 ms Interaction stop at NRd = 72Price = 124, Duedate = 4 Price = 151.9, Duedate = 8 No negotiation resultBU = 0.49, SU = 0.445 BU = 0.319, SU = 0.233

Select SA2

Hybrid protocol (3) Request arrival at NRd = 3 (6) Request arrival at NRd = 3 Request arrival at NRd = 3Refuse arrival at NRd = 4 Agree arrival at NRd = 4 Agree arrival at NRd = 4Switch to HP_S at NRd = 5 Interaction block at NRd = 12 Agreement at NRd = 75Agreement at NRd = 69 Switch to HP_S at NRd = 13 Time consumed = 2765 msTime consumed = 2390 ms Agreement at NRd = 94 Price = 149.3, Duedate = 5Price = 124, Duedate = 4 Time consumed = 2672 ms BU = 0.445, SU = 0.367BU = 0.49, SU = 0.445 Price = 151.9, Duedate = 8 Select SA3

BU = 0.319, SU = 0.233

G. Wang et al. / Information Sciences 282 (2014) 1–14 9

Based on the aforementioned VE environment and the parameter settings, three types of negotiation protocols, themobile protocol, the stationary protocol and the hybrid protocol have been executed. Table 4 records the negotiation resultsof one simulation run for each negotiation protocol, including the negotiation round (NRd), the agreed final negotiation issuevalues, and the buyer utility (BU) and seller utility (SU). It can be observed that only the hybrid negotiation protocol canensure a resultful negotiation in different negotiation scenarios. While for the simpler mobile protocol, no negotiation resultis attained when immigration is refused (BA11 versus SA1) or interaction is suddenly blocked (BA21 versus SA2); for the sim-pler stationary protocol, no final negotiation result is achieved when an agent goes out of time (BA22 versus SA3). At thispoint, the hybrid negotiation protocol is verified to be more vigorous than simpler protocols.

10 G. Wang et al. / Information Sciences 282 (2014) 1–14

In general, if negotiation can converge in accordance with the negotiation protocol, the negotiation result only dependson the utility functions and concession methods adopted by the buyer and seller agents, but not the protocol. On thecontrary, if the negotiation protocol cannot thoroughly support each negotiation pair to reach agreement, the negotiationresults will be different, although the utility functions and concession methods settings are the same. As it can be seen inthe experiment, if the negotiation process has not been interrupted or blocked, the negotiation results for different negoti-ation protocols are the same under the same utility functions and concession methods setting (as situation (2) and (3) inTable 4). Otherwise, if the negotiation process is interrupted because the protocol cannot support the negotiation to converge(as situation (4) and (5) in Table 4), the negotiation results are different from the one which supports a successful negotiation(as situation (6) in Table 4).

Fig. 8(a) and (b) display segments of interaction tracing diagrams of the hybrid negotiation protocol for the one-to-oneand one-to-many negotiation scenarios in JADE. The message sequences in Fig. 8 are in accordance with the interactionregulations defined in the hybrid negotiation protocol.

5.2. Comparison experiments

5.2.1. Test the sub-protocols switching effectExperiments are designed to test the effect on negotiation results when switching between sub-protocols HP_S and

HP_M. Two agents BA21 and SA2 as configured in Table 3 (but with prolonged negotiation time limit) are involved. Here,BA21 uses different concession step limit in experiment group A, B and C, see Table 5. SA2 is designed to block mobile agent’sinteraction at some point when BA21 is in SA2’s host. Relying on the hybrid protocol, BA21 should move back to its originalhost and resume negotiation when the interaction blocking is identified. In Table 5, experiments A-1, B-1 and C-1 are sta-tionary negotiations, while the rest are hybrid negotiations in which BA21 prepares to move to SA’s host at the beginningof negotiations (HP_S ? HP_M). For experiments A-2 and A-3, the mobile interactions are blocked at negotiation rounds 8and 47 respectively. For experiments A-4 and A-5, the mobile interactions are blocked at round 72, but with different net-work situation causing different finishing time. For experiments B2, B3 and B4, the mobile interactions are blocked at nego-tiation rounds 8, 48 and 96 respectively, while there is no interaction blocking in experiment B-5. Cases in experiment groupC are similar to group B.

Observing from Table 5, when the mobile interaction is blocked at some round of negotiation (as listed in the fourthcolumn of Table 5), BA21 will switch to HP_S to continue the next round of negotiation or finalize the negotiation (in A-4

(a) One-to-one negotiation scenario

(b) One-to-many negotiation scenario

Fig. 8. Agent interaction tracing diagram in JADE.

Table 5Negotiation results in different sub-protocol switching scenarios.

BA21 No. HP_S ? M Blocking HP_M ? S Time@seller host Agreement Total time

Concession step limit 25 A-1 n n n n (180.0,7) 7235 msA-2 Nrd 5 Nrd 8 Nrd 9 1375 ms (180.0,7) 7219 msA-3 Nrd 5 Nrd 46 Nrd 47 1875 ms (180.0,7) 8859 msA-4 Nrd 5 Nrd 72 Nrd 72 1797 ms (180.0,7) 8313 msA-5 Nrd 5 Nrd 72 Nrd 72 2203 ms (180.0,7) 13703 ms

Concession step limit 90 B-1 n n n n (140.5,7) 7250 msB-2 Nrd 5 Nrd 8 Nrd 9 860 ms (140.5,7) 6547 msB-3 Nrd 5 Nrd 48 Nrd 49 1812 ms (140.5,7) 7985 msB-4 Nrd 5 Nrd 96 Nrd 97 2656 ms (140.5,7) 5422 msB-5 Nrd 5 None None 2187 ms (140.5,7) 3640 ms

Concession step limit 120 C-1 n n n n (131.7,7) 15422 msC-2 Nrd 5 Nrd 12 Nrd 13 1250 ms (131.7,7) 10625 msC-3 Nrd 5 Nrd 54 Nrd 55 2406 ms (131.7,7) 8359 msC-4 Nrd 5 Nrd 94 Nrd 95 2234 ms (131.7,7) 9094 msC-5 Nrd 5 None None 2594 ms (131.7,7) 5063 ms

G. Wang et al. / Information Sciences 282 (2014) 1–14 11

and A-5). The negotiation agreements (price, due date), stay consistent in one experiment group, which means the switchingbetween sub-protocols will not influence the final negotiation agreement as long as the protocol can support the negotiationto converge. It can be seen from the total negotiation time that negotiation is more efficient when HP_M works throughoutthe negotiation procedure (in B-5 and C-5). Since time costs have not been considered in the utility functions, the switchingbetween sub-protocols will only influence the efficiency of negotiations, but not the negotiation agreements.

5.2.2. Test the efficiency of the ontology operation protocolIn Fig. 9, the embedded ontology operation protocol presented in Section 4.3 (OOP1) is compared with another applicable

ontology operation protocol (OOP2) to test if the former one is more feasible in the mobile agent negotiation system. InOOP1, buyer and seller agents embody the message translation function. In OOP2, a translator agent is introduced to performthe message translation function, while the buyer and seller agents can directly use the messages translated by the translatoragent. In this case, the buyer and seller agents only register their own negotiation ontologies (BNO and SNO respectively).

To compare the efficiency of OOP1 and OOP2, negotiations between BA22 and SA3 are executed utilizing both OOP1 andOOP2. Ten contrast experiments are conducted. The negotiation time consumed is compared in Fig. 10. Generally, negotia-tion time consumed to reach the final agreement in OOP1 is half of that in OOP2. It means that the originally proposed OOP1embedded in the hybrid negotiation protocol, whereby each pair of negotiating agents performing ontology translation bythemselves, is more efficient in dealing with mobile agent negotiations. In comparison, a centralized translator agent inOOP2 has to deal with numerous pairs of negotiating agents simultaneously. It is likely that the centralized translator agentwill be overloaded with information processing task. Such situation could affect the efficiency of negotiation.

The negotiation protocol proposed in this paper is under the assumption that the negotiating agents would like to sharetheir insensitive negotiation ontologies, while keep their sensitive ontologies private. The negotiating agents must hold amutually agreed negotiation ontology matching list before negotiation. However, the detailed method of ontology matchingis not the focus of this paper. The agreement of negotiation ontology correspondences can be reached through iterative delib-erating the similarities of the attributes of related ontology items and the values of some exemplified item individualsbetween negotiating agents.

Buyer Seller

BNOmessage

Translate

Translate

SNOmessage

BNOmessage

Buyer Seller

BNOmessage

SNOmessage

BNOmessage

Translator

Translate

Translate

SNOmessage

Host 1 Host 1Host 2 Host 2Host 3

BNO SNO matching list BNO SNO matching list BNO SNO

<OOP1> <OOP2>

Fig. 9. Two ontology operation protocols.

Fig. 10. The comparison of negotiation time for OOP1 and OOP2.

12 G. Wang et al. / Information Sciences 282 (2014) 1–14

6. Related works

6.1. Multi-agent system based VE applications

Some MAS architectures have been developed for VEs. Petersen, Divitini, and Matskin [33] modeled VEs using the AGORA(AGent Oriented Resource mAnagement) multi-agent architecture, in which agents represent the VE partners and AGORAsare facilitators of cooperative work for agents. Petersen et al.’s later work [34] implements the multi-agent architecture tosupport the formation of a VE. In Gou et al.’s work [20], three types of agents, activity agents, role agents and resource agentsare defined considering the VE operation process. Choi, Kim, and Dob [9] proposed a multi-agent based task assignmentsystem for VEs, executing the selection of individually managed partners and the optimal task assignment. Zhang et al.[48] developed a discrete particle swarm optimization based algorithm to solve the overlapping coalition formation problemin MAS based multiple virtual enterprises. These researches construct the MAS architectures for certain VE organizationphases, but agent interaction mechanisms have not been highlighted. Comparing with them, the proposed VE architecturein this paper focuses more on the interaction issues between agents in the system.

6.2. Mobile agents

For mobile agents applications in the supply chain domain, the advantages have been discussed focusing on the distrib-uted, dynamic and intelligent attributes, facilitating parallel processing and so on [31,42]. The MoCAAS (mobile collaborativeauction agent system) mechanism [28] is an auction agent system using a collaborative mobile agent and a brokering mech-anism. By dispatching the bid-agent to the selected auctioneer-agent to bid autonomously, this mechanism is able to reduceuser operations and network load. The MoRVAM (mobile reverse Vickrey auction model) automated negotiation model [37]uses collaborative mobile agents mediating the buyer and sellers to execute bidding asynchronously and autonomously.Mobile agents have also been employed in an automated outsourcer selection supply chain system to convey orders andcollect capacity information [8]. Although there are evidences for efficiency improvement of mobile agent based systems,the mobile agent technology has not yet achieved the anticipated commercial success, mainly due to the security concerns[12,18]. Therefore, the security issues have been especially considered when utilizing mobile agents in this paper.

6.3. Agent negotiation protocols

Based on the CNP (Contract Net Protocol) regulation, bilateral and multilateral agent negotiation protocols have beenproposed in B2B (Business to Business) or B2C (Business to Customer) e-commerce, e-procurement and supply chain orderfulfillment negotiations [22,27,29,36]. The buyer–seller bilateral negotiation protocol [40] supports the fundamental nego-tiation scenario in which the buyer and seller bargain iteratively on multiple negotiation issues. Adopting this protocol,agents’ decision making methods are studied using the genetic algorithms [10], the heuristic negotiation concession func-tions [32], the incomplete information inference [25], or the constraints-based fuzzy rules [7].

For multilateral negotiation protocols, they have been structured as auctions or many one-to-one bilateral negotiations[27,46,47]. As an early attempt, the Michigan Internet AuctionBot [47] provides a general purpose Internet auction serverthat formalizes multi-agent negotiations in terms of auctions. However, it is more powerful to convert the multilateral

G. Wang et al. / Information Sciences 282 (2014) 1–14 13

negotiation to many one-to-one bilateral negotiations, because it can support multiple rounds of biddings so as to achieve abetter agreement. The ECNPro (Extended Contract Net Protocol) [46] adopts the multi-threaded approach to allow the buyerto bargain concurrently with many sellers by conducting bilateral sub-bargaining.

The negotiation protocol proposed in this paper also perceives the one-to-many multilateral negotiation in the view ofmany one-to-one bilateral negotiations, to be different, the one-to-one negotiation scheme can be either between mobileagent and static agent or between static agents only.

6.4. Ontology-based negotiation knowledge organization

As ontology is capable of integrating descriptive knowledge, procedural knowledge and reasoning knowledge [6], someontology-mediated approaches have been proposed to make agents adapt to various negotiation mechanisms by represent-ing the negotiation protocol in an ontological manner. Tamma et al. [41] presented an ontological approach to automatednegotiation, particularly suited to open environments. In their work, the negotiation protocol is defined in terms of sharednegotiation ontology instead of being hard-coded within agents. Ji et al. [24] also proposed an ontology framework fornegotiation protocols and used an extended Colored Petri Net to verify the concepts and their relationships in the ontology.Viewing from the aspect of agent-based automated procurement, Giovannucci et al. [19] extended the ontology presented byTamma et al. [41] to facilitate multi-item, multi-unit combinatorial reverse auctions. There are also works focusing on theheterogeneity problems between agents using different ontologies in the dynamic open environment [11,31]. Diggelen et al.[11] proposed a layered communication protocol for agents to achieve mutual understanding by establishing minimal andeffective shared ontologies. Using a markup language, ontologies can be represented via Web Ontology Language (OWL),which enables richer expressive power. Ideally, these approaches can ensure the communications and interoperabilitybetween agents.

7. Conclusions and recommendations

This paper presents a model for a new negotiation protocol for buyer–seller type negotiations in the context of virtualenterprises. To support the interaction and collaboration within the VE, a new MAS architecture has been introduced. VEinteractions, involving mainly negotiations between the VE initiator and its VE partners in reality, are represented as one-to-many multilateral buyer–seller negotiations in the proposed MAS. The main novelties of the proposed MAS architectureinclude: (i) the VE initiator can be represented by either stationary or mobile agents in negotiations; and (ii) negotiationknowledge representation is established with the concept of ontology. Accordingly, a hybrid negotiation protocol supportingagent mobility has been developed. This protocol can govern a flexible combination of one-to-one and one-to-many nego-tiations in both stationary and mobile negotiation patterns. To initiate the negotiation process, a one-to-many multilateralnegotiation is decomposed into a number of concurrent one-to-one bilateral negotiations. Each bilateral negotiation mayinvolve ‘‘mobile agent-to-stationary agent’’ or ‘‘stationary agent-to-stationary agent’’ type of interaction, depending onthe circumstance and the collaboration relationship between the VE partners. Two sub-protocols have been defined for thesetwo types of bilateral negotiations; and the protocol is equipped with a sub-protocol switching decision function to deal withdifferent negotiation environments and opponents. Besides, the knowledge expression pattern in the agent negotiation pro-cess has also been refined with the concept of ontology. An embedded ontology operation protocol is developed to ensureontology interoperability.

The validity of the hybrid negotiation protocol has been verified with a hypothetical VE negotiation case. The experimen-tal results show that this protocol is more robust than simpler negotiation protocols.

Besides the application domain of VE, the proposed hybrid agent negotiation protocol can also be used in any situationwhere automated negotiation is needed to form a common agreement or allocate some resources, such as E-commerce,manufacturing scheduling, traffic flow management, social resource allocation, and so on. In respect to various applicationenvironments, the proposed protocol can be modified concerning the accessibility of hosts in the network and the decisionmaking mechanism. That is, the involving parties may not perform as the roles of sellers and buyers, the ontology structureand content may be different and the decision functions of agents may be changed. But these changes will never influencethe message sequences in the protocol; it is still appropriate for agents to negotiate under the proposed protocol.

For future work, the proposed MAS architecture and hybrid negotiation protocol have to be implemented in a VE supportsystem for real-world VE application. In this regard, more effort will be made on the buyer agent consulting mechanism andthe security mechanism to improve the robustness of the negotiation protocol. In addition, empirical case studies will beconducted to evaluate the practical relevance of the approach.

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