TITLE:
An E-Negotiation Agent Using Rule Based and Case Based Approaches: A Comparative Study with Bilateral E-Negotiation with Prediction
AUTHORS:
Sheetal R. Vij, Amruta More, Debajyoti Mukhopadhyay, Avinash J. Agrawal
KEYWORDS:
Automated Negotiation, Multi-Agent, Rule Based Reasoning, Case Based Reasoning, Cloud Computing
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.8 No.10,
October
12,
2015
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.