An E-Negotiation Agent Using Rule Based and Case Based Approaches: A Comparative Study with Bilateral E-Negotiation with Prediction

Abstract

The research in the area of automated negotiation systems is going on in many universities. This research is mainly focused on making a practically feasible, faster and reliable E-negotiation system. The ongoing work in this area is happening in the laboratories of the universities mainly for training and research purpose. There are number of negotiation systems such as Henry, Kasbaah, Bazaar, Auction Bot, Inspire, and Magnet. Our research is based on making an agent software for E-negotiation which will give faster results and also is secure and flexible. The negotiation partners and contents between the service providers change frequently. The negotiation process can be transformed into rules and cases. Using these features, a new automated negotiation model for agent integrating rule based and case based reasoning can be derived. We propose an E-negotiation system, in which all product information and multiple agent details are stored on the cloud. An E-negotiation agent acts as a negotiator. Agent has user's details and their requirements for a particular product. It will check rules based data whether any rule is matching with the user requirement. An agent will see case based data to check any similar negotiation case matching to the user requirement. If a case matches with user requirement, then agent will start the negotiation process using case based data. If any rule related requirement is found in the rule base data, then agent will start the negotiation process using rule based data. If both rules based data and cases based data are not matching with the user requirement, then agent will start the negotiation process using Bilateral Negotiation model. After completing negotiation process, agent gives feedback to the user about whether negotiation is successful or not. The product details, S. R. Vij et al. 522 rule based data, and case based data will be stored on the cloud. So that system automatically becomes flexible. We also compare E-negotiation agent automated negotiation and behavior prediction system to prove that using rule based and case based approaches system should become fast.

A Study of Integrative Bargaining Model with Argumentation-Based Negotiation

“…Automated negotiation research revealed several benefits that computerized negotiation can offer to e-markets. These benefits include: (1) better deal (win-win settlement); (2) decreased transaction cost and time associated with human operation; (3) increased efficiency of settlements even for semi-structured, multi-issue business bargaining problems; and (4) minimizing of some negative aspects of human negotiation, such as avoiding face-to-face encounters with people who are uncomfortable with "bargaining" [5,16,17]. Unfortunately, a poorly designed automated negotiation agent will not be able to deal effectively with a skillful human counterpart that can adopt flexible strategies [5,17].…”

Automated Negotiation in E Commerce: Protocol Relevance and Improvement Techniques

“…Our main focus in this paper is on how effectively and optimally few of the existing negotiation mechanisms (bargaining-bilateral/multilateral, Bidding, Auctioning, multi strategy) can be suitably implemented with techniques like decision support systems, linear programming and form a consolidated research material on various strategies with set of rules/protocols with few assumptions. We are presenting few results and detailed analysis on a working of bilateral negotiation protocol using behavior prediction in decision support system [Bala, Vij and Mukhopadhyay (2015)]; multilateral negotiation protocol using linear programming [Vij, Patrikar, Mukhopadhyay et al (2015)]; Negotiation using Rule based and case based reasoning protocol [More, Vij and Mukhopadhyay (2015)].…”