Sheetal Vij

Agent Based Negotiation Using Cloud – An Approach in E-Commerce

’Cloud computing’ allows subscription based access to computing. It also allows storage services over Internet. Automated Negotiation is becoming an emerging, and important area in the field of Multi-Agent Systems in E-Commerce. Multi-Agent based negotiation system is necessary to increase the efficiency of E-negotiation process. Cloud computing provides security and privacy to the user data and low maintenance costs. We propose a Negotiation system using cloud. In this system, all product information and multiple agent details are stored on cloud. Both parties select their agents through cloud for negotiation. Agent acts as a negotiator. Agents have user’s details and their requirements for a particular product. Using user’s requirement, agents negotiate on some issues such as price, volume, duration, quality and so on. After completing negotiation process, agents give feedback to the user about whether negotiation is successful or not. This negotiation system is dynamic in nature and increases the agents with the increase in participating user.

NAAS: Negotiation Automation Architecture with Buyer’s Behavior Pattern Prediction Component

In this era of “Services” everywhere, with the explosive growth of E-Commerce and B2B transactions, there is a pressing need for the development of intelligent negotiation systems which consists of feasible architecture, a reliable framework and flexible multi agent based protocols developed in specialized negotiation languages with complete semantics and support for message passing between the buyers and sellers. This is possible using web services on the internet. The key issue is negotiation and its automation. In this paper we review the classical negotiation methods and some of the existing architectures and frameworks. We are proposing here a new combinatory framework and architecture, NAAS. The key feature in this framework is a component for prediction or probabilistic behavior pattern recognition of a buyer, along with the other classical approaches of negotiation frameworks and architectures. Negotiation is practically very complex activity to automate without human intervention so in the future we also intend to develop a new protocol which will facilitate automation of all the types of negotiation strategies like bargaining, bidding, auctions, under our NAAS framework.

Negotiation Life Cycle: An Approach in E-Negotiation with Prediction

With the exponential increase in the use of web services it has become more and more important to make the traditional negotiation process automated and intelligent. Various tactics have been given till date which determines the behavior of the software agents in the negotiation process. Here we have given lifecycle of the negotiation process and presented a custom scenario to understand it better. Recently the active area of research has been prediction of partner’s behavior which enables a negotiator to improve the utility gain for the adaptive negotiation agent and also achieve the agreement much quicker or look after much higher benefits. In this paper we review the various negotiation methods and the existing architecture. Although negotiation is practically very complex activity to automate without human intervention we have proposed architecture for predicting the opponents behavior which will take into consideration various factors which affect the process of negotiation. The basic concept is that the information about negotiators, their individual actions and dynamics can be used by software agents equipped with adaptive capabilities to learn from past negotiations and assist in selecting appropriate negotiation tactics.

Intelligent Agent for Prediction in E-Negotiation: An Approach

With the proliferation of web technologies it becomes more and more important to make the traditional negotiation pricing mechanism automated and intelligent. The behavior of software agents which negotiate on behalf of humans is determined by their tactics in the form of decision functions. Prediction of partners behavior in negotiation has been an active research direction in recent years as it will improve the utility gain for the adaptive negotiation agent and also achieve the agreement much quicker or look after much higher benefits. In this paper we review the various negotiation methods and the existing architecture. Although negotiation is practically very complex activity to automate without human intervention we have proposed architecture for predicting the opponents behavior which will take into consideration various factors which affect the process of negotiation. The basic concept is that the information about negotiators, their individual actions and dynamics can be used by software agents equipped with adaptive capabilities to learn from past negotiations and assist in selecting appropriate negotiation tactics.

Automated negotiation with behaviour prediction

The proliferation of web technologies has made it increasingly important to make the traditional negotiation pricing mechanism automated and intelligent. Negotiation is although a complex activity to automate without human intervention but the software agents when enhanced with learning techniques can better simulate the human intelligence and increase the profits of their owners. Prediction of partners behaviour in negotiation will not only improve the utility gain for the adaptive negotiation agent but also achieve the agreement much quicker. The basic concept is that the information about negotiators, their individual actions and dynamics can be used by software agents who are equipped with adaptive capabilities so that they can learn from past negotiations and provide assistance for selection of appropriate negotiation tactics. In this paper an automated negotiation system has been proposed which is capable of predicting the strategy and the preferences of the opponent.