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

Sheetal R. Vij¹, Amruta More¹, Debajyoti Mukhopadhyay², Avinash J. Agrawal³

¹Department of Computer Engineering, Maharashtra Institute of Technology, Pune, India.
²Department of Information Technology, Maharashtra Institute of Technology, Pune, India.
³Department of Computer Science and Engineering, Ramdeobaba College of Engineering and Management, Nagpur, India.

DOI: 10.4236/jsea.2015.810049                

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, 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.

Keywords

Automated Negotiation, Multi-Agent, Rule Based Reasoning, Case Based Reasoning, Cloud Computing

Conflicts of Interest

The authors declare no conflicts of interest.

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