The fuzzy logic based intelligent negotiation agent. This is able to interact autonomously and consequently save human labor in negotiations. The aim of modeling a negotiation agent is to reach mutual agreement efficiently and intelligently. The negotiation agent is able to negotiate with other such agents, over various sets of issues, on behalf of the real-world parties they represent, i.e. it can handle multi-issue negotiation.
The reasoning model of the negotiation agent has been implemented partially by using c# based on Microsoft .NET. The reliability and the flexibility of the reasoning model are finally evaluated. The results show that performance of the proposed agent model is acceptable for negotiation parties to achieve mutual benefits.
Software agent technology is widely used in agent-based e-Commerce. These software agents have a certain degree of intelligence, i.e. they can make their own decisions. The agents interact with other agents to achieve certain goals. However,software agents can not directly control other agents because every agent is an independent decision maker. Negotiation becomes the necessary method to achieve mutual agreement between agents.This focuses on modeling multi-issue, one-to-one negotiation agents for a third party driven virtual market place.We consider one-to-one negotiation because it is the characteristic of individual negotiations and because it allows cooperative negotiation which is not suitable for many-to-many auction based negotiations.
When building autonomous negotiation agents which are capable of flexible and sophisticated negotiation, three broadareas need to be considered:
Negotiation protocols – the set of rules which govern the interaction
Negotiation issues – the range of issues over which agreement must be reached
Agent reasoning models – the agents employ to act in line with the negotiation protocol in order to achieve their negotiation objectives.
This reasoning model aims at the negotiation process. The process of matching and hand shaking in a pre-negotiation process has been solved in several papers. We assume that the buyer agent and vendor agent have roughly matched their similarity and start a negotiation on issues which they have not reached agreement. In a certain round of negotiation, the negotiation agent can pre-prepare a counter offer for the next round of negotiation. The counter offer is generated by the new offer generation engine. Both the incoming offer from the opponent negotiation agent and the counter offer are sent to the offer evaluation block. This does the analysis of the offer and calculates the degree of satisfaction (acceptance of the agent)of the incoming offer and the counter offer. The result is scaled over the range from 0 to 100. Finally, the decision making block makes the decision. It could be acceptance of the current incoming offer, rejection of the current incoming offer, or counter offer.