Issues in automated negotiation:protocol and ontology

In the environment of e-commerce, agents in automated negotiation should share common concept of what they are bargaining and a rule of how to bargain. State of the art overviews of automated negotiation is given. The main barriers to automated negotiation such as protocol and ontology are discussed. Then, a model of automated negotiation is presented with the ontologies of roles and goods described by web ontology language （OWL）, the proposal strategies based on the information sets, and a set of rules for agent interaction. In this model, agents coming from different organizations can negotiate automatically based on common ontologies defined by OWL and formal protocol. This makes it possible for the automated negotiation to be performed in an open environment such as Internet, not merely in a closed system.

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.

Agent Modeling of User Preferences Based on Fuzzy Classified ANNs in Automated Negotiation

In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.

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.

Agent based automated trust negotiation model

To enhance the practicability of the trust negotiation system, an agent based automated trust negotiation model （ABAM） is proposed. The ABAM introduces an agent to keep the negotiation process with no human intervention. Meanwhile, the ABAM specifies the format of a meta access control policy, and adopts credentials with flexible format to meet the requirements of access control policies instead of disclosing the whole contents of a certificate. Furthermore, the ABAM uses asymmetric functions with a high security intensity to encrypt the transmitting message, which can prevent information from being attacked. Finally, the ABAM presents a new negotiation protocol to guide the negotiation process. A use case is studied to illuminate that the ABAM is sound and reasonable. Compared with the existing work, the intelligence, privacy and negotiation efficiency are improved in the ABAM.

Negotiation Model for Shopping Agents in E-marketplace

Automated negotiation is the key techniques for reaching agreements in agent-mediated electronic commerce. Current automated negotiation models assume that users know the value of the product or service they want to buy and provide their agents with a reservation price, and the agents make offers and reach agreements with other agents according to this reservation price. However, in real world electronic marketplaces users probably do not know the exact value of the item, which is in terms of price, and the reservation price they set to their agents only means the maximum price they are willing to pay for the item. In this paper, we propose a negotiation model to deal with the valuation problem. The shopping agent in our model can deliberate the market price and the seller agent’s reservation price from public available information and the seller agent’s proposals. Also in order to conform with the real world negotiation conditions, we introduce negotiation features of real world human shopping such as multiple sellers, valuation, ultimatum, and learning from available information, etc. into our model.

Integrating Interaction Framework for Agent Negotiation

Agent negotiation has become increasingly important since the advent of electronic commerce.There are two kinds of interactions in the process of agent negotiation.One is the interaction between different agents,and the other is the interaction between the agent and the human user.In this paper,firstly,we introduce Q language,a scenario description language for designing interactions between agents and human users.Then we propose an integrating interaction framework for agent negotiation,in which both kinds of interactions are described by Q scenario.Our framework can make the interaction process open and easy to be understood by the users.Users can understand how the negotiation process goes and what is happening in the system including some erroneous or inappropriate actions caused by the negotiation agent.This gives the users a chance to terminate or change the behavior of negotiation agent in time to avoid unfavorable negotiation outcome.In addition,users can customize the agent's behaviors through visible interactions with it.

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.

A PRIORITY-BASED NEGOTIATIONS APPROACH FOR HANDLING INCONSISTENCIES IN MULTI-PERSPECTIVE SOFTWARE REQUIREMENTS

Inconsistency of multi-perspective requirements specifications is a pervasive issue during the requirements process. However, managing inconsistency is not just a pure technical problem. It is always associated with a process of interactions and competitions among corresponding stakeholders. The main contribution of this paper is to present a negotiations approach to handling inconsistencies in multi-perspective software requirements. In particular, the priority of requirements relative to each perspective plays an important role in proceeding negotiation over resolving inconsistencies among different stakeholders. An algorithm of generating negotiation proposals and an approach to evaluating proposals are also presented in this paper, respectively.

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.