New E-Commerce Model Based on Multi-Agent Automated Negotiation

A new multi-agent automated negotiation model is developed and evaluated, in which two competitive agents, such as the buyer and seller, have firm deadlines and incomplete information about each other. The negotiation is multi-dimensional in different cases. The model is discussed in 6 kinds of cases with different price strategies, warrantee strategies and time strategies. The model improves the model of Wooldridge and that of Sycara to a certain extent. In all possible situations, the optimal negotiation strategy is analyzed and presented, and an e-commerce model based on multi-agent automated negotiation model is also illustrated for the e-commerce application in the future.

Automated Negotiation in E Commerce: Protocol Relevance and Improvement Techniques

We all negotiate,formally or informally,in jobs,in day today lives and outcomes of negotiations affect those processes of life.Although negotiation is an intrinsic nature of human psyche,it is very complex phenomenon to implement using computing and internet for the various purposes in E Commerce.Automation of negotiation process poses unique challenges for computer scientists and researchers,so here we study how negotiation can be modeled and analyzed mathematically,what can be different techniques and strategies or set of rules/protocols to be implemented and how they can be relevantly implemented.We are in a quest to find out how this complex process,which involves human psyche can be automated using computers and modern day technologies.Now,the quest is not only automation,looking at the research in the related field in last ten years;but it is all about finding solutions to make e-negotiation more efficient and more accurate,as well as useful in any kind of electronic trading situations.Here is an attempt to consolidate our work of last few years on automation of negotiation process;we call it as negotiation protocol on research,study as well as implementation level of negotiation automation.Overall,we are trying to give few solutions to make the automation more efficient.

Resolving conflicts between negotiation success and sensitive information protection in automated trust negotiation

Automated trust negotiation （ATN） is an approach to establishing mutual trust between strangers wishing to share resources or conduct business by gradually requesting and disclosing digitally signed credentials. In ATN, there are conflicts between negotiation success and sensitive information protection, that is, these two needs cannot be given priority at the same time, which is a challenging problem to resolve. In this paper, a language independent ATN framework, which is dynamic, flexible and adaptive, is presented to address this problem, ensuring negotiation success without sensitive information leakage. This framework is independent of the policy language which is used. However, the language used should have the capability to specify all kinds of sensitive information appearing in credentials and policies, and support the separation of attribute disclosure from credential disclosure. Thus definitions of new language features, which can be incorporated into existing policy languages, are given, enabling the used language to support the capabilities mentioned above.

A MACHINE LEARNING APPROACH FOR MECHANISM SELECTION IN COMPLEX NEGOTIATIONS

Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the charaeteristics of the underlying negotiation problem （e.g. on the complexity of participant＇s utility functions, as well as the degree of conflict between participants）. While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by （1） defining a set of scenario metrics to capture the relevant features of negotiation problems, （2） evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, （3） applying machine learning techniques to identify which mechanisms work best with which scenarios, and （4） demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.

An efficient trust negotiation strategy towards the resource-limited mobile commerce environment

Automated trust negotiation （ATN） offers an at- tractive means for trust establishments, which establishes mu- tual trust among strangers wishing to share resources or con- duct business, but it comes at the cost of non-trivial computa- tion and communication overheads. The deployment of ATN strategies on a resource-constrained mobile device may lead to user-obstructive latency for operations. In this paper, we propose a trust negotiation strategy called trust target Petri nets negotiation strategy （TPNNS）. It highly reduces the ne- gotiation latency in the mobile device compared with other negotiation strategies, since it considers all the alternative re- sponses at each step and chooses the best one. TPNNS sup- ports cycle avoidance and employs skipped TPN which is a new approach presented in this paper. What is more, it is complete and ensures no irrelevant credentials are disclosed during the trust negotiation.