2354 A Roadmap for Ambient E-Service ambient environments. The ambient e-service DSSOLFDWLRQVDUHFDWHJRUL]HGUHÀHFWLQJWKHLBS types (transaction service, information service, navigation, and tracking service and safety ser- vice) as well as exhibiting dynamic collected efforts based on the peer-to-peer design. :H¶OOWDNHWKH³$PELHQWVKRSSLQJPDOOVFH- nario” for example. The Ambient shopping mall VFHQDULRLVFODVVL¿HGDVDGLVWULEXWHGWUDQVDFWLRQ service. In a shopping mall (fully equipped with wireless network infrastructures), information items (e.g., advertisement or sales promotion information) can be broadcast to passing-by peers with information broadcast station. Peers in different locations receive different information items depending on their preference. This means the experience and attained information items of peers are different to their locations and user context (e.g., user preference or interest). That is, the attained information items of a peer vary based on the peer’s interactions with the shopping mall ambient environments. The customers are not required to go all over the shopping mall to receive the broadcast information items, but just pay little money to acquire a suitable information items service package based on their preferences. Alternatively, a mobile agent peer for customer can inquire with nearby peers for what they want and proceed a bartering process. This will help WKHQHZHQWHULQJFXVWRPHUVLQKXUU\WRHI¿FLHQWO\ acquire the shopping mall information. This scenario delineates not only the case of new customers with high buyer perishability (entering the shopping mall and being in a rush to buy certain items without the knowledge of where to buy and how to buy cheap given relevant sales promotion), but also carry out the collective efforts of mobile users (e.g., collective bargaining, collective buy, or some collective agreement). Through a transaction e-service, information items (e.g., e-coupons) can be distributed not only to the mobile users falling into the broadcast range of the distributor companies, but also to the primary target peers (who really need the certain e-coupons in the right time and right place). However, in such an ad-hoc structured environment, peers might not recognize each other. Should we trust the entire information sources? There are no evidences that all the peers are trustable. What if there is someone trying to acquire my sensitive information? There is a pos- sibility of act of swindling; hence, users should protect themselves from any possible forms of harm. Considering a mobile user’s willingness of participation, the safety and privacy issue remain t h e m a jo r c o n c e r n . I f t h e n u m b e r o f p a r t i c i p a n t s o f an e-service diminishes, the e-service application would collapse. Accordingly, a convenient and safe environment would encourage users to participate and interact with each other. Since different e-service applications should cope with different circumstances and bear different restrictions, the framework of ASEM outlines the guidelines for the implementation of ambient e-service. For example, if the trading process employs a bartering mechanism (that does not involve real money), the required level of trust is comparatively lower than those using RI¿FLDOFXUUHQF\,QRWKHUZRUGVYDULRXVDPELHQW e-service applications are of particularly different concerns of the factors outlined in ASEM and lead to different ambient e-service implementation. ASEM also enables diminishing the chance of fraud and deceit. Mobile users can obtain necessary decision information of certain assured quality from nearby sources (e.g. mobile users, service providers). Different information sources are exerted to facilitate great utilities derived in b eh al f of u se rs. On ce t he r isk le vel of t r a ns ac tio ns can be curtailed, the convenience interactions of ambient e-services would be more prevalent and aggrandizing the chances of realizing the power of the collective efforts between mobile users. Platform Design Domain In this section, we bring up some ideas for the future design of ambient e-service platforms (that constitute dynamic identity management, ambient 2355 A Roadmap for Ambient E-Service data access control, seamless unlinkability management, and convenience data access control). Dynamic identity management and ambient data access control particularly concern the nature of an ambient e-service’s environment (e.g., wireless communication distance, handheld device storage capacity, and temporary identity). For the example of the shopping mall scenario application, the communication ranges of the ad-hoc wireless networks centered on a mobile user vary from place to place. The mobile user is required to update the surrounding nearby peer list at their current location, and check if there are RWKHUSHHUV¶RIIHULQJV¿WWLQJWKHLUQHHGV7KDWLV the information update of the surrounding peers is necessary. However, the desired update type (update frequency) varies between applications. In the shopping mall application, it is not necessary to engage a constant update of the list of the surrounding peers because the movement of a user often is not so fast. Accordingly, a periodical update type is a right choice of the update-type design for the ambient shopping mall scenario. This short-term lifetime identity is a unique property in ambient environments. As men- tioned in the ASEM section, Dynamic Identity Management aims to issue different identities for a mobile peer when the peer leaves the en- vironment and re-enters the environment again (even though the mobile device used is the same). However, existing P2P systems/solutions are still with long-lived identities. For instance, as addressed in Resnick, Zeckhauser, Friedman, and Kuwabara (2000), reputation systems generally take on three properties: (1) entities are long-lived; (2) feedback about current interactions is captured and distributed; (3) past feedback guides buyer decisions. In other words, the identities in ambi- ent e-service environments are short-lived and localized, and thus existing methods/solutions requiring long-lived identities can not be applied to our environments. On the other hand, the nature of ambient environment (localized/short period lifetime’s identities) could result in the material change of the reputation’s basics as well as other is- sues (trust/traceability/privacy). How to derive a reputation system coping with the nature of ambient environments accordingly becomes new a challenge to straighten out. Regarding seamless unlinkability management, the requirements for different ambient e-service scenarios are also different. In the ambient shopping mall scenario, if a transaction involves just information items, the amount of necessary information required would be less than those transactions that involve real money. Required security level for privacy concern (e.g., identity tracing back concern, transaction records) can be handled by associating weights with respect to a user’s unique needs and circumstances. A blind signature method provides higher untraceable level than the pseudo identity. Alternatively, a user may have various role identities for different transactions, and this then involves both dynamic identity management and seamless unlinkability management. Convenience data access control facilitates the ambient e-service realization. A single sign-on authorization is more acceptable than those complex authorization processes. While the identity authentication can be achieved by various techniques (e.g., Strong authentication, password, etc.), the proper method is based on a users’ unique needs and preference. Respecting the heterogeneity of data sources, since all data sources have their own risk levels (e.g., risk probabilities), carefree heterogeneous data sources should draw upon the entire data sources so as to enable the computation required for decision making. This computation takes into account the risk level, heterogeneity, and the quantity of available data. However, an economic evaluation method is indispensable due to the computational limitation of ambient handheld devices. 2356 A Roadmap for Ambient E-Service %HQH¿WVRIASEM ASEM aims to provide the design guidelines of the platforms/infrastructures for supporting ambient e-services with a safety and trustworthy environment as well as congregating the collective effort of mobile users within the environment. ,QWKLVVHFWLRQWKHEHQH¿WVRIASEM from the VRFLRHFRQRPLFSHUVSHFWLYHDUHEULHÀ\GLVFXVVHG LQDGGLWLRQWRWKHMXVWL¿FDWLRQRIASEM rendered being technologically possible as addressed in previous sections). From the economic view for privacy invasion, anecdotal evidence shows that people are willing to disclose personal information for potential monetary savings (Russell, 1989), and people do join Web sites for free gifts and catalogs. Those evidence supports that individuals respond to economic incentives in deciding whether to dis- close information. On the other hand, in various organizational and marketing contexts, concern of privacy invasion have been shown to depend on information control, outcomes arising from disclosures, information type and sensitivity, per- ceived relevancy of information use, and target of disclosures. Hoffman, Novak, and Peralta (1999) claimed that nearly 63% of consumers would not provide information to Web sites owing to lack of trust. From the socio-economic view, our method LV WR EH HYDOXDWHG LQ WHUPV RI D FRVWEHQH¿W DQDO\VLVDQGH[SHFWWKHPDMRULW\EHQH¿WZRXOG eliminate privacy invasion. In other words, with our method privacy of a person’s persona would be appropriately protected because all the real personal identities are hidden. Seamless Unlinkability Management enables users to control their owned information in accord with the information type and sensitivity; users are able to decide whether to disclosure the information or not, as well as the target of their information disclosed to. Furthermore, users within the ambient envi- ronment may provide various data sources (i.e., experience or subjective opinions) for others to make a strategic decision. These collective efforts encourage the building of the sense of ambient trust by engaging the reliability of fraud detection in ambient e-service environments. However, some systematical costs are required. Making decisions with heterogeneous data source provide a comparative reliability rather than depending on their own information, especially in the dynamic environment. When the number of peers exceeds the limit of computation capability, the complexity of data management and computation will become a major problem especially in a Peer-to-Peer environment. For preliminary estimates, establishing a trustworthy ambient environment with privacy protection PLJKW KDYHWR WUDGHZLWKVRPH HI¿FLHQF\ ORVV Therefore, the trade-off between the cost and EHQH¿W LV D PDMRU LVVXH IRU IXUWKHU UHVHDUFK At the moment, this chapter mainly focuses on the framework prospect, but we intend to provide a vision of collective wisdom within ambient e-service environment. However, the implementation or systematical evaluations are not the focus of this chapter. In summary, with the supporting infrastructures (delimited by ASEM), ambient e-service applications are believed to bring us a new carefree information/service era. Let’s take the shopping mall scenario for example. Mobile users can acquire desired information items very conveniently. That is, even a user communicating with other unknown mobile users, there is still measurable data for the user to consider, such as numerous nearby unknown users’ experience or opinions gathered to serve as a reference material for advanced transaction decisions. Users can determine their actions based on various informa- tion sources, and decide which one is suitable for their current needs. Fraud and untruthful activities can also be diminished with the collective effort from those participants. 2357 A Roadmap for Ambient E-Service CONCLUSION Ambient e-services address dynamic collective efforts of mobile users dynamically engaging interactions in the ambient environments, ren- dering a new paradigm of mobile commerce promising revolutionary business models. This chapter presents an ambient e-services framework characterizing three supporting stacks. The am- bient value stack describes the value process in ambient environments. The ambient technology VWDFNLGHQWL¿HVWKHWHFKQRORJ\SURFHVVWRHQVXUH connectivity and security in ambient interactions and cooperation between peers and then realize powerful collective efforts. The environment stack then represents the ambient basics for the collaborations. Ambient e-services applications can be divided into two types. One is for the distributed trading; another is for the distributed collaboration. How- HYHUVRFLDOFRQWH[WDQGVLJQL¿FDQWUDSLGJURZWK of connections enabled by P2P are the two major incentives for applying ambient e-service to such revolutionary business models. We exemplify several ambient e-service applications. Those ap- plications differ from existing mobile e-services (grounded on client/server design) in terms of the focus of the dynamic interactions between peers in dynamic ambient e-service environments. In this chapter, we also present another framework called Ambient e-Service Embracing Model (ASEM) that addresses the core elements (of relevance to the integrated concern of trust, reputation and privacy) required for assuring such desired features as convenience, safety, fairness and collaboration for mobile users when they engage ambient e-services. This framework manifests the relationship between the issues of dynamic identity management and ambient data management. The framework abstracts the trust, reputation, and privacy concerns into an integrated consideration. Since different e- service applications are of different circumstances and bear different restrictions, the framework of ASEM also outlines the guidelines for the implementation of ambient e-service applications and the platforms. The fruitful future research includes a further in-depth evaluation of ASEM, a complete design of the ASEM core elements, and a ¿HOGHG LPSOHPHQWDWLRQ RI FHUWDLQ ambient e- services deriving economic models of ambient e-services. REFERENCES Aberer, K., & Despotovic, Z. (2001). Managing trust in a peer-2-peer information system. In Pro- ceedings of the Conference on Information and Knowledge Management ( pp. 310 -317). CIK M01. November 5-10, 2001, Atlanta, Georgia. $VKUD¿ 1 .XLOERHU - 3 3ULYDF\ protection via technology: Platform for privacy preferences (P3P). International Journal of E- Business Research, 1(2), 56-69. Baida, Z., Gordijn, J., Akkermans, H., Saele, H., & Morch, A. Z. (2005). Finding e-service offerings by computer-supported customer need reasoning, International Journal of E-Business Research, 1(3), 91-112. Castelfranchi, C., Falcone, R., & Pezzulo, G. (2003). Integrating trustfulness and decision using fuzzy cognitive maps. 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A pessimistic approach to trust in mobile agent platforms. IEEE Internet Computing (pp. 40-48), September-October. This work was previously published in the International Journal of E-Business Research, Vol. 3, Issue 1, edited by I. Lee, pp. 51-73, copyright 2007 by IGI Publishing (an imprint of IGI Global). 2360 Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 8.2 A Survey on Neural Networks in Automated Negotiations Ioannis Papaioannou National Technical University of Athens, Greece Ioanna Roussaki National Technical University of Athens, Greece Miltiades Anagnostou National Technical University of Athens, Greece INTRODUCTION Automated negotiation is a very challenging UHVHDUFK¿HOGWKDWLVJDLQLQJPRPHQWXPLQWKHH business domain. There are three main categories RIDXWRPDWHGQHJRWLDWLRQVFODVVL¿HGDFFRUGLQJWR the participating agent cardinality and the nature of their interaction (Jennings, Faratin, Lomuscio, Parsons, Sierra, & Wooldridge, 2001): the bilat- eral, where each agent negotiates with a single opponent, the multi-lateral which involves many providers and clients in an auction-like framework and the argumentation/persuasion-based models where the involving parties use more sophisticated arguments to establish an agreement. In all these automated negotiation domains, several research efforts have focused on predicting the behaviour of negotiating agents. This work can be classi- ¿HGLQWZRPDLQFDWHJRULHV7KH¿UVWLVEDVHGRQ techniques that require strong a-priori knowledge concerning the behaviour of the opponent agent in previous negotiation threads. The second uses mechanisms that perform well in single-instance negotiations, where no historical data about the past negotiating behaviour of the opponent agent is available. One quite popular tool that can sup- port the latter case is Neural Networks (NNs) (Haykin, 1999). NNs are often used in various real world ap- plications where the estimation or modelling of a function or system is required. In the automated negotiations domain, their usage aims mainly to enhance the performance of negotiating agents in predicting their opponents’ behaviour and thus, achieve better overall results on their behalf. This paper provides a survey of the most popular automated negotiation approaches that are us- 2361 A Survey on Neural Networks in Automated Negotiations ing NNs to estimate elements of the opponent’s behaviour. The rest of this paper is structure as follows. The second section elaborates on the state of the art bilateral negotiation frameworks that are EDVHGRQ11V7KHWKLUGVHFWLRQEULHÀ\SUHVHQWV the multilateral negotiation solutions that exploit NNs. Finally, in the last section a brief discussion on the survey is provided. NEURAL NETWORKS IN BILATERAL NEGOTIATIONS In (Zhang, Ye, Makedon, & Ford, 2004) a hy- brid bilateral negotiation strategy mechanism is described that supplies negotiation agents with PRUHÀH[LELOLW\DQGUREXVWQHVVLQDQDXWRPDWHG negotiation system. The framework supports a dynamically assignment of an appropriate ne- gotiation strategy to an agent according to the current environment, along with a mechanism to create new negotiation rules by learning from past negotiations. These learning capabilities are based on feedforward back-propagation neural networks and multidimensional inter-transac- tion association rules. However, the framework LVQRWDGHTXDWHO\GHVFULEHGDQGGH¿QHGDQGWKH QHXUDOQHWZRUNVDUHQRWVSHFL¿FDOO\LQVWDQWLDWHG Additionally, there are neither quantitative nor qualitative experimental results for real world cases. Finally, the format of the input to the ge- neric network that is presented is ambiguously described. In (Zeng, Meng, & Zeng, 2005), the authors employ a neural network to assist the negotia- WLRQRYHUYHU\VSHFL¿FLVVXHVIURPDUHDOZRUOG example. The network is trained online by the past offers made by the opponent, while both the buyer and the seller agent have the ability to employ the proposed network. However, the experimental data sets are very restrictive and do not address the diversity of those that can be arisen in real scenarios. Additionally, the authors do not present the actual size of the hidden layer, a parameter that is extremely crucial with regards to the appropriateness to use such a network in a real time negotiation procedure by an agent with limited resources. Furthermore, in (Rau, Tsai, Chen, & Shiang, 2006), the authors studied the negotiation pro- cess between a shipper and a forwarded using a learning-based approach, which employed a feedforward back-propagation neural network with two input data models and the negotiation decision functions. Issues of the negotiation were the shipping price, delay penalty, due date, and shipping quantity. The proposed mechanism was applicable to both parties at the same time and the network architecture was chosen based on past similar attempts, following a very restrictive pat- tern for the number of the hidden layer’s neurons. The conducted experiments showed an overall improvement of the results for both negotiating parties, while the framework was proven stable and with small deadlock probability. However, as its authors support, further experimentation is required especially with regards to a wider variety of strategies and possibly more suitable network architectures for the hidden layer. In (Carbonneau, Kersten, & Vahidov, 2006), a neural network based model is presented for predicting the opponent’s offers during the ne- gotiation process. The framework was tested RYHUDVSHFL¿FVHWRIH[SHULPHQWDOGDWDFROOHFWHG from other existent frameworks and it is highly adjusted to these data. The purpose of this solution is not only to predict the opponent’s next offer, EXWDOVRWKHSHUFHSWLRQIRUWKHVSHFL¿FSURFHGXUH i.e. an overall vision on why everything is hap- pening and where the procedure is led. Thus, the prediction of the opponent’s next round offer is only a part of the network’s output. However, the chosen experiment set is constrained and doesn’t examine the effectiveness of the framework on diverse strategies as those proposed in the very ¿UVWVWHSVRIWKHDUHDDQGDUHQRZPDLQO\XVHG (Faratin, Sierra, & Jennings, 1998). Additionally, 2362 A Survey on Neural Networks in Automated Negotiations although the authors support the view that their framework is proper for real-time environments, WKHIDFWLVWKDWWKHUHVXOWHGQHWZRUNLVGLI¿FXOWWR be online trained, mainly because of its size and the resources that are required for such training. Thus, this network architecture is probably inap- propriate for mobile agents’ environments, and VRPHWKLQJVPDOOHUDQGPRUHVSHFL¿FVKRXOGEH designed, due to the limitations that these envi- ronments share. Moreover, in (Oprea, 2003), the author presents a shopping agent, which is capable of negotiating in online bilateral, multi-issue procedures using DQRIÀLQHFUHDWHGDQGWUDLQHGIHHGIRUZDUGQHXUDO QHWZRUNLQRUGHUWRLQFUHDVHLWVSUR¿WDELOLW\E\ adapting its behaviour according to its opponent’s. The purpose of the neural network’s application on each procedure is to predict the opponent’s next offer on a round by round basis and thus, model LWVEHKDYLRXUDQG LQWHQWLRQV LQRUGHU WR¿QDOO\ achieve a better or even the best possible deal. With the exploitation of the neural network the shopping agent can decide during the online phase of negotiation, which is the opponent’s strategy and estimate its reservation value. Concerning the experiments conducted, the author uses the ZHOOMXVWL¿HG QHJRWLDWLRQ WDFWLFV SUHVHQWHG LQ (Faratin, Sierra, & Jennings, 1998) in order to test the proposed solution and concludes that the framework is working well in case of medium or long term agents’ deadlines. However, the results SUHVHQWHGDUHQRWWKRURXJKO\MXVWL¿HGDQGPRUH extreme opponent strategies should be tested in order to decide on the network’s adequacy for such environments. Probably, the three hidden OD\HU QHXURQV PLJKW QRW EH VXI¿FLHQW IRU VXFK cases and long-term estimations. Finally, Papaioannou et al. have recently designed and evaluated several single-issue bilateral negotiation approaches, where the Cli- ent agent is enhanced with Neural Networks. 0RUH VSHFL¿FDOO\ LQ 5RXVVDNL 3DSDLRDQQRX & Anagnostou, 2006), the Client agent uses a lightweight feedforward back-propagation NN coupled with a fair relative tit-for-tat imitative tactic, and attempts to estimate the Provider’s price offer upon the expiration of the Client’s deadline. This approach increases the number of agreements reached by one third in average. In (Papaioannou, Roussaki, & Anagnostou, 2006), the performance of MLP and RBF NNs towards the prediction of the Provider’s offers at the last round has been compared. The experiments indi- cate that the number of agreements is increased by ~38% in average via both the MLP- and the RBF-assisted strategies. Nevertheless, the overall time and the number of neurons required by the MLP are considerably higher than these required by the RBF. In (Roussaki, Papaioannou, & An- agnostou, 2007), MLP and GR NNs have been used by the Client agent in order to identify the unsuccessful negotiation threads (UNTs) at an early stage, thus terminating them long before the deadlines expire. It has been observed that the MLP NN detects more than 90% of UNTs in average, outperforming by little the GR NN. Fi- nally, in (Papaioannou, Roussaki, & Anagnostou, 2007), the performance of MLP and RBF NNs has been compared with cubic splines, least-square- based polynomial approximators, exponential approximators and Gaussian approximators, in order to predict the future offers of the negotiating Provider Agent. The wide experimental evalua- tion conducted indicates that both the MLP- and the RBF-assisted negotiation strategies perform almost equally well and outperform the other four approximator-assisted strategies. In this paper, the proposed framework is extended to address multi- LVVXHQHJRWLDWLRQVFRQVLGHULQJWKHVLJQL¿FDQFHRI the issues under negotiation for the negotiating party, as well as their degree of interdependency. A disadvantage in the aforementioned NN-based negotiation frameworks is that they have only been evaluated in case the Provider agent adopts a time-dependent strategy. 2363 A Survey on Neural Networks in Automated Negotiations NEURAL NETWORKS IN MULTILATERAL NEGOTIATIONS In (Oprea, 2001), the use of a small-scaled feed- forward neural network is attempted in order to predict the opponent agent’s behaviour. In this framework the enhanced agent is negotiating against an opponent that is not equipped with any learning or other intelligent mechanism. The neural network is properly constructed and trained at every round to respond with the opponent’s next value at each negotiation step using only the three prior offers issued by the opponent. This fact makes the step-by-step computation feasible in real time procedures, but not neces- sarily reliable. However, the proposed approach was proved adequate only in cases when either both agents (or at least the opponent agent) have long-term deadlines. A different usage of the neural networks’ potential is presented in (Shibata, & Ito, 1999), where the authors are mainly concerned with the communication between agents. In principal, they divide the agents’ communication into two FODVVHVZLWKUHVSHFWWRLWVPHDQLQJ7KH¿UVWRQH incorporates the cases where the agent transmits the observed information while the second those where the agent’s intention is transmitted. The framework exploits an Elman recurrent neural network with feedback loops, especially for the latter class of cases. The network assists the agents to avoid possible negotiation deadlocks, although nothing is known apriori with regards to their strategy or resources. The network keeps the past information and adapts online its corresponding agent’s behaviour accordingly in order to avoid collisions. The proposed framework was also tested with four agents leading to promising re- sults. However, the authors don’t propose or apply WHFKQLTXHVIRUKLJKHUSUR¿WDELOLW\RIWKHSDUWLFL- pating agents but only for collision avoidance by learning the opponent’s intention. Additionally, a recurrent neural network is a complex structure and seems inappropriate for application in low resources agent environments. Furthermore, in (Abreu, Canuto, & Santana, 2005) the authors present a comparative analysis of some negotiation methods used in a multi-neu- ral agent system, called NeurAge. This system LVFRPSRVHGRIVHYHUDOQHXUDOFODVVL¿HUVFDOOHG neural agents, and its main aim is to overcome VRPHGUDZEDFNVRIPXOWLFODVVL¿HUV\VWHPVDQG as a consequence, to improve their performance. These neural agents provide a common output, which results after negotiation among them and it is the system’s output. For this purpose, three different negotiation methods are evaluated: the game theoretic, the auction based and the con- ¿GHQFHEDVHGRQHV7KHUHVXOWVSURYHWKDWWKH SURSRVHGDSSURDFKLVYDOXDEOHIRUVXFKFODVVL¿HU systems and might end up being valuable in cases ZKHUHWDFWLFFODVVL¿FDWLRQVKRXOGEHFRQGXFWHG However, the system is inappropriate for online procedures, requires cooperation between mul- tiple neural agents and has not been tested on real negotiation tactics’ numerical data. Therefore, the UHVXOWV PLJKWEH YDOXDEOHZKHQD FODVVL¿FDWLRQ scheme is required, but are probably inappropriate as a future prediction pattern. On the other hand, (Veit, & Czernohous, 2003) present the results of enhancing consumer agents with several machine-learning algorithms in a properly designed electronic market with one static VXSSOLHU7KHUHVXOWVSURYHWKDWXQGHUYHU\VSHFL¿F circumstances the neural network assisted agent performs worse than a simple Q-learning assisted DJHQWWKDWPDLQWDLQVDVSHFL¿FVHWRIYDOXHVIRU the learning procedure in an a-priori instantiated matrix. However, the scenarios are very restric- tive and in no case address the characteristics of real world ones where the application of similar table based agents would fail mainly due to the diversity of the potential solution spaces for each negotiation. Besides, the authors themselves admit this remark, including it in their future plans. In (Park, & Yang, 2006), the authors propose a negotiation agents system based on the incremen- . convenient and safe environment would encourage users to participate and interact with each other. Since different e-service applications should cope with different circumstances and bear different. Context- aware computing applications. In Proceedings of the 1 st I n t e r n a t i o n a l Wo r k s h o p o n M o b i l e C o m p u t - ing Systems and Applications (pp. 85-90). Shand, B., Dimmonck,. hand, in various organizational and marketing contexts, concern of privacy invasion have been shown to depend on information control, outcomes arising from disclosures, information type and