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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 EURZVHUVWKDWODFNÀH[LELOLW\IRUFXVWRPHUVWR 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-
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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. 5th 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. InternationalConference 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 (2nd 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. 2nd 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. 4th International Symposium
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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. 2nd International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2003), Melbourne, 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. 5th 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).
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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 relate to front-end e-business activities that involve 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
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
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.
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