A genetic agent-based negotiation system

Automated negotiation by autonomous agents has become increasingly important since the advent of e-marketplace. In this study, automated negotiation is viewed as a search process in which negotiators jointly search for a mutually acceptable contract in a multidimensional space formed by negotiable issues. This search is formulated as a multiple-objective decision making problem and is solved through an iterative process of generating offers by fuzzy inference systems. These fuzzy inference systems serve as a search heuristic and are formulated based on the strategy of issue trade-offs. Five experiments are conducted to evaluate the performance of the proposed automated negotiation algorithm.

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Intelligent agents for e-marketplace: negotiation with issue trade-offs by fuzzy inference systems

Towards a web services and intelligent agents-based negotiation system for B2B eCommerce

B2C e-commerce is becoming more widespread as more people come to recognize its convenience and its ability to rapidly respond to requests and as more products and services become available. However, many electronic marketplaces, especially in the business-to-consumer, are in essence some kind of search engine where buyers look for the best product in a database of products offered by sellers. Usually, such e-marketplaces do not use agent technology at all although agents could significantly improve the services provided both for the buyers and the sellers. Further, negotiation capabilities are essential for B2C e-commerce systems. In an automated negotiation, intelligent agents engage in broadly similar processes to achieve the same end. In more detail, the agents prepare bids for and evaluate offers on behalf of the parties they represent with the aim of obtaining the maximum benefit for their users. Nevertheless, in the current situation, price is the only criterion by which agents are created. This factor is easy to measure and automate. However, the criteria for advanced transactions need to be elaborated, for example, details of giveback and dividend. In this paper, we present a multiple-attributes negotiation model for B2C e-commerce, which deploys intelligent agents to facilitate autonomous and automatic on-line buying and selling by intelligent agents while quickly responding to consumers. These include a 4-phase model, information collection, search, negotiation, and evaluation. We also apply fuzzy theory and analytical hierarchy process to develop the system interface to facilitate the user inputs. Finally, an example of the notebook purchasing process is illustrated.

The agent-based negotiation process for B2C e-commerce

This article presents the design, implementation and application of Mobile-C, an IEEE Foundation for Intelligent Physical Agents (FIPA) compliant agent platform for mobile CsC++ agents. IEEE FIPA standards are a set of specifications designed to ensure the interoperation between agents in a heterogeneous network. Mobile-C conforms to the FIPA standards both at agent and platform level. Mobile-C extends FIPA standards to support mobile agents by integrating an embeddable CsC++ interpreter—Ch—into the platform as a mobile agent execution engine and defining a mobile agent mobility protocol to direct agent migration process. Agent migration in Mobile-C is achieved through FIPA agent communication language (ACL) messages encoded in XML. Using FIPA ACL messages for agent migration provides a straightforward but effective way for inter-platform agent migration in FIPA compliant agent systems as both agent communication and migration can share the same communication mechanism. Choosing scriptable CsC++ as a mobile agent language allows mobile agents easy interfacing with low-level software modules and underlying hardware. Mobile-C has been used to simulate highway traffic detection and management. The agent-based traffic detection and management system uses agent technology for real-time distributed traffic information fusion. Mobile agents in the system are used for dynamic code deployment and performing unanticipated actions. The application of agent technology shows a great potential for enhancing the interoperability, flexibility and distributed computing capabilities of intelligent transportation systems. Copyright © 2006 John Wiley & Sons, Ltd.

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Mobile-C: a mobile agent platform for mobile C-C++ agents

The market mechanism design is important for both conventional and electronic commerce as it affects the fairness and efficiency of trading. In this paper, we propose a new market mechanism for time- constrained trading. Our mechanism mimics the traditional brokering system, where buyers and sellers meet together and negotiate through an electronic marketplace. At each time step, agents are paired up for negotiating a deal. We consider that buyer and seller agents are associated with a time constraint and a search cost, and thus must decide promptly and judiciously whether to accept an offer. One distinguishing feature of our mechanism is that the marketplace provides buyer and seller agents with statistical information about the goods. Specifically, the statistics include the probability distributions of obtaining and losing a particular offer. With this information, the agents' decision can be viewed as a Markov decision process, and the optimal (dominant) trading strategy can be computed. In other words, buyer and seller agents can make trading decisions that maximize their expected utility without the need of speculating others actions. We also explain how such a dominant strategy can be computed in an efficient manner. Another distinguishing feature of the proposed mechanism is that the statistics are updated continuously and hence the dominant trading strategy is adaptive. Experimental results verify that our mechanism facilitates a fair allocation to the trading agents under various demand and supply conditions.

A dynamic mechanism for time-constrained trading

Automated negotiation has become increasingly important since the advent of electronic commerce In an efficient market, goods are not necessarily traded in a fixed price, and instead buyers and sellers negotiate among themselves to reach a deal that maximizes the payoffs of both parties In this paper, a genetic agent-based model for bilateral, multi-issue negotiation is studied The negotiation agent employs genetic algorithms and attempts to learn its opponent's preferences according to the history of the counter offers based upon the stochastic approximation We also consider two types of agents: level- 0 agents are only concerned with their own interest while level-1 agents consider also their opponents' utility Our goal is to develop an automated negotiator that guides the negotiation process so as to maximize both parties' payoff

Samuel P. M. Choi

Papers

A genetic agent-based negotiation system

A dynamic mechanism for time-constrained trading

Evolutionary Negotiation in Agent-Mediated Commerce