2364 A Survey on Neural Networks in Automated Negotiations tal learning of a feedforward neural network in RUGHUWRLQFUHDVHWKHHI¿FLHQF\RIELODWHUDOQHJR- tiations and to improve the applicability towards multilateral negotiations. The network is triggered with values that are extracted after a utility evalu- ation procedure and at each round the output is forming the next counter-offer of the party. With regards to the generalization to the multilateral case, the proposed approach is based on match- ing all sellers and all buyers in pairs among all possible ones, following practical criteria as the common negotiation range term used, indicates. The experimental results show that the proposed system achieves up to 2% more agreements and carries out the negotiations at least twice as fast as others with similar settings. In (Wang, Chai, & Huang, 2005), the authors attempt to solve the problem of selecting a selling agent that meets buyer user’s requirements as well as his utility constraints as those represented by the corresponding intelligent agent. The problem is solved by choosing the seller before the negotia- tion and thus, the accuracy of the negotiation and the buyer’s utility are improved. In order to fully utilize negotiation history, this paper transforms the problem of choosing seller into a K-armed bandit problem. The utility function is a joint summation of the utilities of both the buyers and the sellers, while the buyer uses a properly learned neural network in order to learn its opponents’ SUHIHUHQFHVDQG¿QDOO\FKRRVHWKHRQHWKDWZLOO lead to the best agreement. The advantage of this framework is that the buyer’s neural network learns off-line and only uses the results for the online procedure. Thus, there is not substantial impact on the real procedure. Finally, in (Liu, & You, 2003), a fuzzy neural network is proposed to deal with the uncertain- ties in real world shopping activities, such as FRQVXPHU SUHIHUHQFHV SURGXFW VSHFL¿FDWLRQ product selection, price negotiation, purchase, delivery, after-sales service and evaluation. The fuzzy neural network manages to achieve an DXWRPDWLFDQGDXWRQRPRXVSURGXFWFODVVL¿FDWLRQ and selection scheme to support fuzzy decision- making by integrating fuzzy logic technology and the back-propagation feedforward neural network. In addition, a visual data model is introduced to overcome the limitations of the current web EURZVHUV WKDW ODFN ÀH[LELOLW\ IRU FXVWRPHUV WR view products from different perspectives. The experimental results demonstrate the feasibility of the proposed approach for web-based business transactions. CONCLUSION AND DISCUSSION In this paper, a brief survey of the most popular UHVHDUFKHIIRUWVLQWKH¿HOGRI11DVVLVWHGDXWR- mated negotiations is presented. An important observation that can easily be made is that that there is a substantial diversity on the purposes that the NNs are used for in this domain. For instance, in some cases they aim to estimate the opponent’s future offers, whereas in other cases they assist the negotiating agent on selecting the best tactic that should be used in order to increase its potential utility. Even though the usage of NNs in automated negotiations may enhance various aspects of their performance and results, there are some cases where they are not suitable. For example, they perform far better when they are trained off-line, thus being less suitable when no a-priori knowledge is available. In general, it is preferable that relatively small NNs that are trained off-line are used, but if this is not possible, it is better to use NNs of minimal size that are trained on-line, risking however that they will eventually not be suitable enough. Furthermore, if the negotiation strategy of the opponent is not consistent, thus frequently demonstrating sharp FKDQJHVLQWKHW\SHRUFRQ¿JXUDWLRQRIWKHWDFWLF used, the NNs often fail to adjust. In case the op- ponent employs imitative negotiation strategies, the usability of NNs in estimating the opponent’s behaviour is questionable. Finally, if the agent has low storage and processing resources avail- 2365 A Survey on Neural Networks in Automated Negotiations able, the NNs that can be employed need to be so OLJKWZHLJKWWKDWWKH\FRQVLGHUDEO\ODFNÀH[LELOLW\ Despite these shortcomings, it is expected that NNs will gain a considerable share in the learn- ing-enabled negotiating agents in the electronic marketplace. REFERENCES Abreu, M., Canuto, A., & Santana, L. (2005). A Comparative Analysis of Negotiation Methods for a Multi-neural Agent System. 5 th International Conference on Hybrid Intelligent Systems (HIS 2005), Rio de Janeiro, Brazil. Carbonneau, R., Kersten, G., & Vahidov, R. (2006). Predicting Opponent’s Moves in Elec- tronic Negotiations Using Neural Networks. International Conference of Group Decision and Negotiation (GDN 2006), Karlsruhe, Germany. Faratin, P., Sierra, C., & Jennings, N. (1998). Negotiation Decision Functions for Autonomous Agents. International Journal of Robotics and Autonomous Systems. (24)3-4, 159-182. Haykin, S. (1999). Neural Networks: A Compre- hensive Foundation (2 nd edition). London UK: Prentice Hall. Jennings, N., Faratin, P., Lomuscio, A., Parsons, S., Sierra, C., & Wooldridge, M. (2001). Automated Negotiation: Prospects, Methods, and Challenges. International Journal of Group Decision and Negotiation. (10)2, 199-215. Liu, J., & You, J. (2003). Smart Shopper: An Agent-Based Web-Mining Approach to Internet Shopping. IEEE Transactions on Fuzzy Systems. (11)2, 226-237. Oprea, M. (2001). Adaptability and Embodi- ment in Agent-Based Ecommerce Negotiation. Workshop Adaptability and Embodiment Using Multi-Agent Systems (AEMAS 2001), Prague, Czech Republic. Oprea, M. (2003). The Use of Adaptive Negotia- tion in Agent-Mediated Electronic Commerce. /HFWXUH1RWHVRQ$UWL¿FLDO,QWHOOLJHQFH (LNAI). Springer-Verlag, Berlin Heidelberg New York. 2691, 594-605. Papaioannou, I., Roussaki, I., & Anagnostou, M. (2006). Comparing the Performance of MLP and RBF Neural Networks Employed by Negotiating Intelligent Agents. IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006), Hong Kong, China. Papaioannou, I., Roussaki, I., & Anagnostou, M. (2007). Comparing Polynomial Approximators to Neural Networks for Agent Behaviour Prediction in e-Negotiations, submitted for publication to the ACM Transactions of Autonomous and Adaptive Systems. Park, S., & Yang, S. (2006). An Automated System based on Incremental Learning with Applicability Toward Multilateral Negotiations. International Joint Conference SICE-ICASE, Busan, Korea. Rau, H., Tsai, M., Chen, C., & Shiang, W. (2006). Learning-based automated negotiation between shipper and forwarder. Journal of Computers and Industrial Engineering, (51)3, 464-481. Roussaki, I., Papaioannou, I., & Anagnostou, M. (2006). Employing Neural Networks to Assist Negotiating Intelligent Agents. 2 nd IEE Interna- tional Conference on Intelligent Environments 2006 (IE 2006), Athens, Greece. Roussaki, I., Papaioannou, I., & Anagnostou, M. (2007). Building Automated Negotiation Strategies Enhanced by MLP and GR Neural Networks for Opponent Agent Behaviour Progno- sis. Lecture Notes of Computer Science (LNCS). Springer-Verlag, Berlin Heidelberg New York. 4507, 152-161. Shibata, K., & Ito, K. (1999). Emergence of Communication for Negotiation By a Recurrent Neural Network. 4 th International Symposium 2366 A Survey on Neural Networks in Automated Negotiations on Autonomous Decentralized Systems, Tokyo, Japan. Veit, D., & Czernohous, C. (2003). Automated Bid- ding Strategy Adaptation using Learning Agents in Many-to-Many e-Markets. 2 nd International Joint Conference on Autonomous Agents and Multi-Agent Systems (A A M A S 2 0 03) , M e l b o u r n e , Australia. Wang, L.M., Chai, Y.M., & Huang, H.K. (2005). Choosing optimal seller based on off-line learning negotiation history and k-armed bandit problem. International Conference on Machine Learning and Cybernetics (ICMLC 2005), Guangzhou, China. Zeng, Z.M., Meng, B., & Zeng, Y.Y. (2005). An Adaptive Learning Method in Automated Negotiation. International Conference on Ma- chine Learning and Cybernetics (ICMLC 2005), Guangzhou, China. Zhang, S., Ye, S., Makedon, F., & Ford, J. (2004). A Hybrid Negotiation Strategy Mechanism in an Automated Negotiation System. 5 th ACM Confer- ence on Electronic Commerce (EC 2004), New York, USA. KEY TERMS Automated Negotiation: It is the process by which group of actors communicate with one another aiming to reach to a mutually acceptable agreement on some matter, where at least one of the actors is an autonomous software agent. Bilateral Negotiation: A negotiation proce- dure, where exactly two parties are involved, i.e. a client and a provider. Multilateral Negotiation: A negotiation pro- cedure, where more than two parties are involved, i.e. multiple clients and/or providers negotiate simultaneously. Multi-Layer Perceptron (MLP): A fully connected feedforward NN with at least one hid- den layer that is trained using back-propagation algorithmic techniques. Neural Network (NN): A network modelled after the neurons in a biological nervous system with multiple synapses and layers. It is designed as an interconnected system of processing ele- ments organized in a layered parallel architecture. These elements are called neurons and have a limited number of inputs and outputs. NNs can EHWUDLQHGWR¿QGQRQOLQHDUUHODWLRQVKLSVLQGDWD HQDEOLQJVSHFL¿FLQSXWVHWVWROHDGWRJLYHQWDUJHW outputs. Radial Basis Function (RBF): Function that involves a distance criterion with respect to a centre, such as a circle, ellipse or Gaussian. RBF NN:,WLVDQDUWL¿FLDO11WKHDFWLYDWLRQ functions of which are radial basis functions. ,WKDVWZROD\HUVRISURFHVVLQJZKHUHWKH¿UVW maps the input onto each RBF neuron in the other (hidden) layer. 7KLVZRUNZDVSUHYLRXVO\SXEOLVKHGLQWKH(QF\FORSHGLDRI$UWL¿FLDO,QWHOOLJHQFHHGLWHGE\-'RSLFR-GHOD&DOOHDQG$ Sierra, pp. 1524-1529, copyright 2009 by Information Science Reference (an imprint of IGI Global). 2367 Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 8.3 Patterns for Designing Agent-Based E-Business Systems Michael Weiss Carleton University, Canada ABSTRACT Agents are rapidly emerging as a new paradigm for developing software applications. They are being used in an increasing variety of applica- tions, ranging from relatively small systems such as assistants to large, open, mission-criti- cal systems like electronic marketplaces. One of the most promising areas of applications for agent technology is e-business. In this chapter, we describe a group of architectural patterns for agent-based e-business systems. These patterns r e l a t e t o f r o n t - e n d e - b u s i n e s s a c t i v i t i e s t h a t i n vo l v e interaction with the user, and delegation of user tasks to agents. Patterns capture well-proven, common solutions, and guide developers through the process of designing systems. This chapter should be of interest to designers of e-business systems using agent technology. The description of the patterns is followed by the case study of an online auction system to which the patterns have been applied. INTRODUCTION Agents are rapidly emerging as a new paradigm for developing software applications. They are be- ing used in an increasing variety of applications, ranging from relatively small systems such as assistants to large, open, mission-critical systems like electronic marketplaces. One of the most promising areas of applications for agent tech- nology is e-business (Papazoglou, 2001). In this chapter, we describe a group of architectural pat- terns for agent-based e-business systems. These patterns relate to front-end e-business activities that involve interaction with the user, and delega- tion of user tasks to agents. The chapter is structured as follows. First, we provide a background on patterns and their application to the design of agent systems. Then, we discuss the forces or design constraints that need to be considered during the design of agents for e-business systems. This is followed by a de- scription of the agent patterns for e-business. A 2368 Patterns for Designing Agent-Based E-Business Systems number of examples illustrate the application of these patterns. Finally, we discuss current trends and opportunities for future research and offer concluding remarks. BACKGROUND Patterns are reusable solutions to recurring design problems and provide a vocabulary for com- municating these solutions to others. The docu- mentation of a pattern goes beyond documenting a problem and its solution. It also describes the forces or design constraints that give rise to the proposed solution (Alexander, 1979). These are the undocumented and generally misunderstood features of a design. Forces can be thought of as pushing or pulling the problem towards different solutions. A good pattern balances these forces. A set of patterns, where one pattern leads to other SDWWHUQVWKDWUH¿QHRUDUHXVHGE\LWLVNQRZQDV a pattern language. A pattern language can be likened to a process: it guides designers who wants to use those patterns through their application in an organic manner. As each pattern of the pattern language is applied, some of the forces affecting the design will be resolved, while new unresolved forces will arise as a consequence. The process of using a pattern language in a design is complete when all forces have been resolved. There is by now a growing literature on using patterns to capture common design practices for agent systems. Aridor and Lange (1998) describe domain-independent patterns for the design of mobile agent systems. They classify mobile agent patterns into traveling, task, and interaction pat- terns. Kendall, Murali Krishna, Pathak, et al. (1998) use patterns to capture common build- ing blocks for the architecture of agents. They integrate these patterns into the layered agent pattern, which serves as a starting point for a pattern language for agent systems based on the strong notion of agency. Schelfthout, Coninx, et al. (2002), on the other hand, document agent implementation patterns suitable for developing weak agents. Deugo, Weiss, and Kendall (2001) identify a set of patterns for agent coordination, which are, again, domain-independent. They classify agent patterns into architectural, communication, traveling, and coordination patterns. They also describe an initial set of global forces that push and pull solutions for coordination. Kolp, Gior- gini, and Mylopoulos (2001) document domain- independent organizational styles for multi-agent systems using the Tropos methodology. Weiss (2004) motivates the use of agents through a set of patterns that document the forces involved in agent-based design and key agent concepts. On the other hand, Kendall (1999) reports on ZRUNRQDGRPDLQVSHFL¿FSDWWHUQFDWDORJGHYHO- oped at BT Exact. Several of these patterns are documented using role models in a description of the ZEUS agent building kit (Collis & Ndumu, 1999). Shu and Norrie (1999) and the author in a precursor to this chapter have also documented GRPDLQVSHFL¿FSDWWHUQVUHVSHFWLYHO\IRUDJHQW based manufacturing and electronic commerce. However, unlike most other authors, they present the patterns in the form of a pattern language. This means that the relationships between the patterns are made explicit in such a way that they guide a developer through the process of designing a system. Lind (2002) and Mouratidis, Weiss, and *LRUJLQLVXJJHVWWKDWZHFDQEHQH¿WIURP integrating patterns with a development process, while Tahara, Oshuga, and Hiniden (1999) and Weiss (2003) propose pattern-driven development processes. Lind (2002) suggests a view-based categorization scheme for patterns based on the MASSIVE methodology. Mouratidis et al. (2006) document a pattern language for secure agent systems that uses the modeling concepts of the Tropos methodology. Tahara et al. (1999) propose a development method based on agent patterns and distinguish between macro and micro architecture patterns. Weiss (2003) documents a process for mining and applying agent patterns. 2369 Patterns for Designing Agent-Based E-Business Systems FORCES The design of agent-based systems in the e- business domain is driven by a number forces, including autonomy, the need to interact, infor- mation overload, multiple interface, ensuring quality, adaptability, privacy concerns, search costs, and the need to track identity. Not all of WKHVHIRUFHVFDQEHHTXDOO\VDWLV¿HGE\DJLYHQ design, and trade-offs need to be made. The pat- terns described in this chapter help with making informed trade-offs. AUTONOMY The currently dominant metaphor for interacting with computers is direct manipulation. Direct manipulation requires the user to initiate all tasks explicitly and to monitor all events. For example, a user searches the Web for an auction that of- fers the desired item for sale, and subsequently monitors the state of the auction. The obvious drawback of this approach is that most of the time the user is occupied in tasks that are peripheral to WKHSULPDU\REMHFWLYHV7KHXVHU¶VDELOLW\WR¿QG the best deal available at any of the many online auctions in operation is also greatly limited. Agents can be used to implement a comple- mentary interaction style, in which users delegate some of their tasks to software agents which then perform them autonomously on their behalf. This indirect manipulation style engages the user in a cooperative process in which human and soft- ware agents both initiate communication, moni- tor events, and perform tasks. Autonomy is the capability of an agent to follow its goals without i n t e r a c t i o n s o r c o m m a n d s f r o m t h e u s e r o r a n o t h e r agent. An autonomous agent does not require the user’s approval at every step of executing its task, but is able to act on its own. With agents performing autononmous actions, users are now facing issues of trust and control over their agents. The issue of trust is that by engag- ing an agent to perform tasks (such as selecting a seller), the user must be able to trust the agent to do so in an informed and unbiased manner. The agent should not, for example, have entered contracts with sellers to favor them in return for a cut on their proceeds to the developer of the agent or the server that hosts and executes the agent. The user would also like to specify the degree of autonomy of the agent. For example, the user may not want to delegate decisions to the agent that have OHJDORU¿QDQFLDOFRQVHTXHQFHVDOWKRXJKDEX\HU DJHQWLVFDSDEOHRIQRWRQO\¿QGLQJWKHFKHDSHVW seller, but also placing a purchase order. NEED TO INTERACT Agents typically only have a partial representa- tion of their environment, and are thus limited in their ability—in terms of their expertise, access to resources, location, and so forth—to interact with it. Thus, they rely on other agents to achieve goals that are outside their scope or reach. They also need to coordinate their activities with those of other agents to ensure that their goals can be met, avoiding interference with one another. The behavior of an individual agent is thus often not comprehensible outside its social structure—its relationships with other agents. For example, the behavior of a buyer agent in an auction cannot be fully explained outside the context of the auction itself, and of the conventions that govern it (for example, in which order—ascending or descend- ing—bids must be made, and how many rounds of bidding there are in the auction). An important issue in designing systems of interacting agents is dealing with openness. The Internet and e-business applications over the Internet are both examples of open systems. Open systems pose unique challenges in that their components are not known in advance; they can change unexpectedly, and they are composed of heterogeneous agents implemented by different developers at different times with different tools 2370 Patterns for Designing Agent-Based E-Business Systems a n d m e t h o d o l og ie s . S i m i l a r l y, a s w e d o n o t c o nt r o l all the agents, one can also not assume that the agents are cooperative. Some agents may be be- nevolent and agree on some protocol of interaction, but others will be self-interested and follow their own best interests. For example, in an electronic marketplace, buyer and seller agents are pursuing WKHLURZQEHVWLQWHUHVWVPDNLQJSUR¿WDQGQHHG to be constrained by conventions. INFORMATION OVERLOAD 3HRSOHDQGRUJDQL]DWLRQVZLVKWR¿QGUHOHYDQW information and offerings to make good deals and JHQHUDWHSUR¿W+RZHYHUWKHODUJHVHWRIVHOOHUV in conjunction with the multiple interfaces they XVHPDNHVLWGLI¿FXOWIRUDKXPDQWRRYHUYLHZ the market. One solution has been to provide portals or c om m o n e n t r y p o i nt s t o t h e We b . T h e s e portals periodically collect information from a multitude of information sources and condense WKHPWRDIRUPDWWKDWXVHUV¿QGHDVLHUWRSURFHVV typically taking the form of a hierarchical index. One disadvantage of this solution is that the cat- egories of the index will be the same for every user. Individual preferences are not taken into account when compiling the information, and niche interests may not be represented at all. MULTIPLE INTERFACES 2 Q H RIW KHG L I ¿F X OW LH VL Q¿ Q G L QJL Q IRU P DW LRQ HJ when comparing the offerings of different sellers) is the large number of different interfaces used to present the information. Not only are store fronts organized differently, sellers do not fol- low the same conventions when describing their products and terms of sale. For instance, some sellers include the shipping costs in the posted price; others will advertise one price, but add a handling charge to each order. A solution is to agree on common vocabularies, but these must also be widely adopted. With the introduction of the extensible markup language (XML) for associating metacontent with data and current developments in the Semantic Web such as on- tology representation languages (OWL), this is slowly becoming a reality. For example, a price in a catalog can be marked up with its currency whether it already includes the shipping cost. +RZHYHUWKHGLI¿FXOW\ZLWKDQ\VWDQGDUGIRUPDW is that it takes a considerable amount of time to ¿QG DJUHHPHQW DPRQJ WKH LQWHUHVWHG SDUWLHV One also needs to allay the fear of sellers in los- ing business to competitors once their product information becomes easily accessible. ENSURING QUALITY Shopping online lacks the immediate mechanisms for establishing trustworthiness. How can you trust a seller, with whom you have had no previous encounter, whether the order you placed will be IXO¿OOHGVDWLVIDFWRULO\")RUH[DPSOHDQ\VHOOHULQ an online auction could claim that the item offered for sale is in superior condition, when the buyer cannot physically verify that claim. One solution is to solicit feedback about the performance of a seller (respectively, buyer) from buyers (respec- W L YHO \ V H O OH U V D I W H U R U G H U I X O ¿ OO P H Q W )R U H[D P SOH the online auction site eBay keeps records of how a seller was rated by other buyers. Potential new buyers will take the ratings from previous buy- ers into account before considering buying from a seller. However, eBay’s solution falls short in two ways. Old low ratings are not discarded or discounted when more recent ratings are higher. Also, if a seller gets a low overall rating, it is easy for the seller to assume a new identity and start afresh with a new rating. A mechanism for ensur- ing quality must avoid this, as discussed in the context of reputation system design by Zacharia, Moukas, and Maes (1999). 2371 Patterns for Designing Agent-Based E-Business Systems ADAPTABILITY Users differ in their status, level of expertise, needs, and preferences. The issue of adaptability is that of tailoring information to the features of the user, for example, by selecting the products most suitable for the user from a catalog, or adapting the presentation style during the interaction with the user. Any approach to tailoring information involves creating and maintaining a user model. When creating a user model, two cases need to be distinguished (Ardissono, Barbero, et al., 1999): IRU¿UVWWLPHYLVLWRUVQRLQIRUPDWLRQDERXWWKHP is available, and the user characteristics must be recognized during the interaction; on subsequent visits, a detailed user model about a visitor is already available and can be used to tailor the information. Several design considerations for user model- ing are detailed in Ardissono et al. (1999). Users need to register permanently with the system to have their data stored; otherwise user models will only be maintained during a single interac- tion. In the context of online shopping, a system must also deal with direct and indirect users. A customer of a Web store may browse for products for himself, as well as for other people (indirect users), who have different needs and preferences. )LQDOO\ WKH XVHU PRGHOPXVW EH DEOHWR UHÀHFW changes in interest over time. One approach to collecting user information is to ask the user to provide the information explicitly, for example, E\¿OOLQJRXWDIRUP7KLVDOORZVRQHWRFUHDWHD SUR¿OHRIWKHXVHUWKDWLVSRWHQWLDOO\YHU\DFFXUDWH and to provide personalized service to the user from the beginning. However, there are at least two problems with this solution. First, by requir- ing the user to provide this information upfront; the threshold for the user to do so is very high. Only very advanced users will want to tune their RZQSUR¿OHV6HFRQGZKHQWKHXVHU¶VLQWHUHVWV FKDQJHWKLVZLOOQRWEHUHÀHFWHGLQWKHSUR¿OH XQOHVVWKHXVHUNHHSVXSGDWLQJKHUSUR¿OH$JDLQ L QSUDFWLFHXVHU VGRQ RWXSGD WHWKHLU SU R¿OHVDIWH U installation. PRIVACY CONCERNS Personalization requires the collection and rele- sase of personal information to the agent providing the personalized service. One way of personal- izing interactions between buyers and sellers is for the seller to collect information about a buyer from the buyer’s behavior (e.g., their clickstream). The buyer may not be aware of the information collected, nor does she always have control over what information is gathered about her. Although effective from the seller’s perspective, this is not a desirable situation from the perspective of the buyer. Users are typically not willing to allow just anyone to examine their preferences and usage patterns, in particular without their knowledge or consent. They want to remain in control, and decide on an interaction-by-inter- action basis which information is conveyed to the seller. A solution that addresses the force of privacy concerns must put the user in charge of which information is collected and who it is made available to. An additional complexity results from the desire of some buyers to remain anonymous. If a buyer remains anonymous, a seller cannot provide personalized service. Thus, generally, users are willing to share personal information with sellers, if the expected gains outweigh the possible threats for their privacy. SEARCH COSTS ,WFDQEHH[SHQVLYHIRUEX\HUVDQGVHOOHUVWR¿QG each other. In a static marketplace, each buyer can store a contact list of sellers for each product, and then quickly locate an appropriate seller when a particular product is needed. However, an elec- tronic marketplace is dynamic. Buyers and sellers can join and leave the marketplace, and change 2372 Patterns for Designing Agent-Based E-Business Systems their requirements and offerings qualitatively and quantitatively at any point in time. It, therefore, becomes impossible for a market participant to maintain an up-do-date list of contacts. Another problem is that of restricting the buyer’s options. If each buyer maintains its own list of contacts, they run the risk of not being aware of better deals available elsewhere. One possible solution to these problems is to use a mediator which can match potential trading partners in the market. With the introduction of mediators, buyers and sellers no longer maintain their own lists of contacts, or need to contact a large number of DOWHUQDWLYHWUDGLQJSDUWQHUVWR¿QGWKHRSWLPDO one. One trade-off of this solution is, however, that individual preferences or history of interac- tion with a particular trading partner cannot be accounted for by a mediator. Thus, it is reasonable to maintain individual lists of trading partners that one has dealt with in the past, keeping track of the quality provided and using this personalized UDQNLQJRISDUWQHUVWR¿OWHUWKHOLVWRIFRQWDFWV provided by a mediator. IDENTITY For various reasons, buyers and sellers need to be represented by unique identities. The most important reasons are authentication, nonre- pudiation, and tracking. One way of assigning a unique identity to trading partners is to use one of the many unique labels which are readily available on the Internet, for example, an e-mail address, or a Yahoo! account name. A problem with this approach is that it is also very easy to obtain a new identity, thus making authentication, nonrepudiation, or tracking schemes that rely on such identities impractical. Similarly, a user could obtain multiple identities and pretend to represent multiple different parties, where instead there is only one. A solution that remedies this situation m u s t m a ke i t a dv a nt a g e o u s f o r i n d iv i d u a l s t o k e e p their identities over those users who change them often (Zacharia et al., 1999). PATTERNS 7KHSDWWHUQVZHLGHQWL¿HGDQGWKHLUUHODWLRQVKLSV DUHVKRZQLQ)LJXUH7KHDUURZVLQGLFDWHUH¿QH- ment links between the patterns. Each arrow in WKHGLDJUDPSRLQWVLQWKHGLUHFWLRQIURPD³ODUJHU´ SDWWHUQWRD³VPDOOHU´SDWWHUQ7KHVWDUWLQJSRLQW for the language is the AGENT SOCIETY pattern, which motivates the use of agents for building WKHDSSOLFDWLRQ$WWKHQH[WOHYHORIUH¿QHPHQW the diagram leads the designer to consider the patterns agent as DELEGATE, AGENT AS MEDIATOR, AND COMMON VOCABULARY. 1 agent as delegate and the patterns it links to deal with the design of agents that act on behalf of a single user. The agent as mediator pattern guides the designer through the design of agents that facilitate between a group of agents and their users. COMMON VOCABULARY provides guidelines IRUGH¿QLQJH[FKDQJHIRUPDWVEHWZHHQDJHQWV 7KH UHVW RI )LJXUH VKRZV UH¿QHPHQWV RIWKH AGENT AS DELEGATE pattern. For example, the USER AGENT pattern prescribes to use a single locus of interaction with the user and represent the concur- rent transactions a user participates in as buyer and seller agents. User interaction also includes SUR¿OLQJWKHXVHU USER PROFILING) and subscrib- ing to information (e.g., the status of an auction) relevant to the user ( NOTIFICATION). In the following, each pattern is represented by its context, the problem it addresses, a discussion of the forces, its solution, and a resulting context. The context is represented by the dependencies between the patterns. The problem is a succinct statement on what problem the pattern addresses. The solution takes the form of a role diagram. 7KHVHUROHVZLOOEH¿OOHGE\DJHQWV)RUH[DPSOH consider the USER AGENT pattern. It is applied after AGENT AS DELEGATEDQGLQWXUQUH¿QHGE\USER PROFILING and NOTIFICATION. The it addresses the 2373 Patterns for Designing Agent-Based E-Business Systems problem of how users instruct agents to act on their behalf (as buyers and sellers) and how they k e e p i n c o n t r o l o ve r w h a t t h e a g e n t d o e s (e . g ., d o e s it have authority to complete a trade?). The role diagram for the USER AGENT pattern is shown in Figure 2. Role diagrams and their semantics are discussed further in AGENT SOCIETY. The resulting context points to related patterns in this pattern language. AGENT SOCIETY Context <RXUDSSOLFDWLRQGRPDLQVDWLV¿HVDWOHDVWRQHRI the following criteria: your domain data, control, knowledge, or resources are decentralized; your application can be naturally thought of as a sys- tem of autonomous cooperating entities, or you have legacy components that must be made to interoperate with new applications. Problem How do you model systems of autonomous co- operating entities in software? Forces • Autonomy • Need to interact Solution Model your application as a society of agents. Agents are autonomous computational entities (autonomy), which interact with their environment (reactivity) and other agents (social ability) in order to achieve their own goals (proactiveness). Often, agents will be able to adapt to their environment and have some degree of intelligence, although these are not considered mandatory characteris- tics. These computational entities act on behalf of users or groups of users (Maes, 1994). Thus, Figure 1. Patterns for e-business agents and their dependencies (arrows indicate dependencies and dashed lines patterns that are not described here) . Ndumu, 1999). Shu and Norrie (1999) and the author in a precursor to this chapter have also documented GRPDLQVSHFL¿FSDWWHUQVUHVSHFWLYHOIRUDJHQW based manufacturing and electronic commerce system. Lind (2002) and Mouratidis, Weiss, and *LRUJLQLVXJJHVWWKDWZHFDQEHQH¿WIURP integrating patterns with a development process, while Tahara, Oshuga, and Hiniden (1999) and Weiss (2003). be- nevolent and agree on some protocol of interaction, but others will be self-interested and follow their own best interests. For example, in an electronic marketplace, buyer and seller agents