As e-commerce becomes common a large number of Internet-based systems has been developed to assist users in different aspects of electronic trading. In particular systems that use software agent technologies are proving to be effective in supporting and automating various stages of the trading process (e.g. product finding, supplier finding, product ordering, delivery monitoring etc). More recently software agents have successfully been introduced to support and/or automate negotiation in e-commerce (e-negotiation). Examples of such systems are Kasbah, AuctionBot and Tete-a-Tete. The purpose of such systems is to find a mutual agreement on the terms of transactions that satisfies all parties' constraints, preferences and objectives. Most of agent systems can negotiate on behalf of users based on auctions and other forms of competitive negotiation according to the users' instructions. Some systems can handle aspects of uncertainty associated with incomplete information about other parties. However they typically assume that all information available is precisely defined. For example they usually require the users to provide exact and precise information about their private preferences, constraints and objectives (e.g. price < $99, delivery time = 1 day, etc). Because most real-world negotiation problems can also involve preferences and constraints that may be imprecisely defined (eg. low price, high quality, short delivery time etc) negotiation agents that can also consider imprecise information may be needed. This paper overviews an experimental fazzy e-negotiation agents system FeNAs that can support automated negotiation in the presence of imprecise information. The system uses the principles of hzzy constraint-based reasoning involving fuzzy constraint modeling, satisfaction and propagation. It is demonstrated with a prototype for the used car-trading problem. The system supports multi-issue negotiations where offers consist of a number of issues that can include the price of the car and other value-added services such as warranty and the value of the trade-in car. The agents exchange offers on the basis of the information available and negotiation strategies used by each party, Information available to both the buyer and the seller can include the make, model, color, transmission, age and mileage of the car. Each agent has also some private information including preferences, priorities and financial constraints that are not available to other agents. This information can be imprecise where constraints, preferences and priorities are defined as fuzzy constraints describing the level of satisfaction of an agent (and its user) with different potential solutions. The overall objective of an agent is to find a solution that maximizes the agent's utility at the highest possible level of constraint satisfaction subject to its acceptability by other agents. During negotiation the agents follow a common protocol of negotiation and individual negotiation strategies. The protocol prescribes the common rules of negotiation (e.g. agents are expected to accept own offers, negotiation is successful if the final offer satisfies all parties, issues of negotiation can include the price, warranty and trade-in). The negotiation strategies specify how the agents evaluate and generate offers in order to reach a consensus according to their constraints and objectives. In general the received offers are evaluated and counter-offers are generated with the support of fuzzy constraint-based reasoning. A number of negotiation strategies have been implemented in FeNAS including a shopping strategy, fixed value strategy, simple step strategy, consensus strategy and their better deal versions. 26
The FeNAs platform REF uses fuzzy constraints and permits correlated multiple bilateral negotiations, however, the agent's interactions are positional.
FeNAs: a fuzzy e-negotiation agents system
Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)
Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)
Computer Science