Bireshwar Dass Mazumdar

Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods

The Multi agent system MAS model has been extensively used in the different tasks of e-commerce like customer relation management CRM, negotiation and brokering. For the success of CRM, it is important to target the most profitable customers of a company. This paper presents a multi-attribute negotiation approach for negotiation between buyer and seller agents. The communication model and the algorithms for various actions involved in the negotiation process is described. The paper also proposes a multi-attribute based utility model, based on price, response-time, and quality. In support of this approach, a prototype system providing negotiation between buyer agents and seller agents is presented.

Multiagent Paradigm for the Agent Selection and Negotiation in a B2C Process

Multi-agent paradigms have been developed for the negotiation and brokering in B2C e-commerce .Few of the models consider the mental states and social settings (trust and reputation) but rarely any model depicts their combination. In this work, we present three mathematical models. First cognitive computational model which is combined model of belief, desire, intention (BDI) model for agent’s mental attitudes and social settings; is used for the computation of trust and then index of negotiation which is based on trust and reputation and the second computation model is developed for the computation of business index that characterizes the parameters of some of the business processes, which match the buyer’s satisfaction level. The third computation model of utility is used for negotiation between seller and buyer to achieve maximum combined utility increment (CUI) which is the difference of marginal utility gain (MUG) of buyer and marginal utility cost (MUC) of seller.

On agent mediated coordination strategies and its application in disaster management system

Multi-agent System (MAS) formulations have been developed and explored in the past in a variety of domains, ranging from optimization problems to game theory. However, there are certain issues that are common across all the domains. Cooperation and coordination of agents in MAS is one such important area. In this paper, we have first presented a very brief survey characterizing the cooperation and coordination strategies that have been frequently used. Thereafter the paper progresses to explore the applicability of MAS formulations in disaster management domain. We have identified the characteristics and requirements of disaster management domain; described the ways in which MAS formulations can be used in this domain; and presented the preliminary results of our simulation experiment of a greedy approach to disaster recovery.

Multi-Agent Coalition Formation for Course Selection Strategies in E-Learning System

Multi Agent Systems are being used in a wide variety of applications, ranging from comparatively small systems for personal assistance, to open, complex, systems for industrial applications. In e-learning, Multi Agent Systems appear to be a promising approach to deal with the challenges in educational environments. They can provide new patterns of learning and applications, such as personal assistants, user guides and alternative help systems, which are helpful for both students and teachers. In this work, we presented a multi- agent system based for this e-learning scenario based on course selection theory. We have described here coalition formation among the student agents who are going to select the courses which will be running in the university. We first introduced a novel voting procedure where agents make coalition among them & allocate points to different courses and voting occurs for subject/course in several rounds. This way the agents are able to freely express their preferences and at the same time use the information provided from previous rounds to vote intelligently and strategically. We then introduced different voting strategies for subject/course selection in the university by coalition, and evaluated their performance in a range of scenarios. The results show that even a simple voting strategy provides outcomes which are close to optimal. Furthermore, our intelligent strategy was unable to exploit other, more native voters.

Intelligent profitable seller agent categorization and negotiation in E-Commerce by using data mining method

Multi agent system (MAS) model has been extensively used in the different tasks of E-Commerce such as customer relation management (CRM), negotiation and brokering. This paper presents Data Mining (DM) based seller agent categorization and a multi-attribute negotiation approach for negotiation between buyer and seller agents of same category provided by DM result. The paper also described a multi-attribute based utility model which is based on the price, response-time, and quality. In support of the presented approach a prototype system providing negotiation between buyer agents and seller agents has been reported.

Multi-Agent Negotiation Paradigm for Agent Selection in B2C E-Commerce

Multi agent system MAS model has been extensively used in the different tasks of E-Commerce such as customer relation management CRM, negotiation and brokering. The objective of this paper is to evaluate a seller agents various cognitive parameters like capability, trust, and desire. After selecting a best seller agent from ordering queue, it applies negotiation strategies to find the most profitable proposal for both buyer and seller. This mechanism belongs to a semi cooperative negotiation type, and selecting a seller and buyer agent pair using mental and cognitive parameters. This work provides a logical cognitive model, logical negotiation model between buyer agent and selected seller agent.