Practically “negotiation” can be defined as an iterative process which aims to achieve a mutually beneficial deal for the seller and buyer [1] . The automation saves human negotiation time and computational negotiators are better at finding deals in combinatorial and strategically complex settings. The rapid success of online auctions clearly shows that E-negotiation will eventually become the basis of e-commerce. Whether it is a case of B to B purchase or a case of online shopping, it is required to make the traditional negotiation pricing mechanism automated and intelligent [2] .

Cloud computing is innovation that uses advanced computational power and improved storage capabilities. Cloud computing is a new processing scheme in which computer processing is performed in the network. This means that users need not concern themselves with the processing details. Although Cloud computing enables more flexible, easier and faster computing [3] .

Case Based Reasoning (CBR) is a problem solving paradigm where the solution of new problem is based on solution of similar past problem. We use Rule Based Reasoning (RBR) concept, where there are some rules such as discount, festival offers etc.

In this system, we are introducing an E-negotiation agent based system using Rule Based Reasoning and Case Based Reasoning. Due to the use of Rule Based Reasoning and Case Based Reasoning, system becomes faster. In this system, all data that is product detail, case base data and rule base data are stored on cloud. Therefore system becomes flexible.

In this section, we are presenting literature survey related to only rule based and case based reasoning. In our previous paper [4] , we have referred ten papers related to automated negotiation in the literature survey.

Liu Xiaowen and Yu Jin [5] introduced automated negotiation model for tourism industry. To improve the negotiation efficiency and success rate, this system proposed RBR and CBR. The model employs CBR method to support an automated negotiation by past successful negotiation cases used for those negotiation partners that have no contract rule existing in each other. This system does not support multi party multi issue negotiation.

Mohammad Irfan Bala, Sheetal Vij and Debajyoti Mukhopadhyay [6] introduced, E-negotiation system with behavior prediction. This work reviews the various methods used for predicting the opponent’s behavior and then proposes architecture for behavior prediction using artificial neural networks. It proposes the use of database for storing the results and suggests various issues that can be taken into consideration while predicting the opponent’s behavior.

Mira Vrbaski and Dorina Petriu [7] proposed Context-aware systems which use Rule Based Reasoning engines for decision making without involving explicit interaction with the user. It is difficult to rank suitable solutions based on unclear, qualitative criteria with a rule based approach, while rule based systems excel in filtering out unsuitable solutions based on clear criteria.

Leen-Kiat Soh and Costas Tsatsoulis [8] use Case Based Reasoning (CBR) and utility to learn, select, and apply negotiation strategies. Agent uses Case Based Reasoning (CBR) approach to solve new problem of negotiation strategy which based on previous similar past problem. Agent also learns from its previous negotiation experience.

P. Maes, R. Guttman, A. Moukas [9] introduced a Kasbah negotiation model. In this system, agents can only negotiate over the single issue of price. However, B2B negotiations often involve multiple issues. Moreover, the Kasbah agents can only act according to one of their pre-defined negotiation strategies which may not lead to the optimal negotiation results.

P. Wurman, M. Wellman, W. Walsh [10] introduced the Michigan AuctionBot is a general purposed Internet- based auction server hosted by the University of Michigan. Sellers can create new auctions on AuctionBot by choosing from a set of pre-defined auction types and then enter their specific auction parameters such as clearing time, minimum bid increment and whether proxy bids are allowed. E-bay is the example of AuctionBot negotiation system.

Some of above papers support multi party multi issue negotiation Rule Based Reasoning and Case Based Reasoning. Our negotiation system is a bilateral, muti-party, multi issue negotiation model. In this system, buyer and seller negotiate o multiple issues at a time and when both buyer and seller comes to final decision, then only negotiation process will be stopped.

The architecture of E-negotiation agent system is shown in Figure 2.

Working of E-negotiation agent system:

1) Databases on cloud

・ Product detail: used to store organizations’ product detail like product name, price, quantity;

・ Case base: used to store experience of agent about negotiation process;

・ Rule base: used to store rules related to product like discount, festival offers.

2) Seller and Buyer: Users of the system

3) Agent: An agent acts as a negotiator

Negotiation Process: After receiving Seller’s and Buyer’s requirement, agent will start negotiation process. For negotiation process, agent will check three conditions.

・ Case based: An agent will see case based data to check any similar negotiation case is matching to the user requirement. If a case matches with user requirement, then agent start the negotiation process using case based data.

・ Rule based: It will check rules based data whether any rule is matching with the user requirement. If any rule related requirement is found in the rule base data, then agent will start the negotiation process using rule based data.

・ Bilateral Negotiation model: If both rules based data and case based data are not matching with the user requirement, then agent will start the negotiation process using Bilateral Negotiation model.

4) Feedback: After completing negotiation process, agent gives feedback to the user about whether negotiation is successful or not.

Flow chart of E-negotiation agent system is shown in Figure 3.

For this system, we can use three components. Using these components, system becomes easy to use and work efficiently.

1) Store data on cloud: For storing data, we can use Open Stack cloud. Open Stack is a Cloud computing project to provide an IaaS. It is free open source software released under the terms of the Apache License.

In this module, all data which is required for negotiation process like product data, rule base data and case base data is stored on cloud.

2) Negotiation process: For negotiation process [4] , seller and buyer give their requirements to agent in encrypted format that is generate the hash code of that requirement and encrypt that hash code using user’s public key. User’s public key is known to Agents.

where, for generating hash function MD5 algorithm is used.

U_pk is user’s public key.

We can use encryption for security purpose, in this process, we can use digital signature concept.

Same as seller, buyer can do same process for generating hash code of his requirement.

After getting requirement, agent decrypt the hash code using his private key and calculate the hash code for checking whether this message comes from appropriate seller or buyer and whether it is modified or not.

where, A_pri is agent’s private key.

After getting the requirement, agent checks the user requirements whether any rule related to requirement is found in the rule base data then only agent will start the negotiation process using decision function [11] .

・ Decision Function: After getting the requirement, agent checks the user requirements whether any rule related to requirement is found in the rule base data then only agent will start the negotiation process using decision function. The negotiation .model introduces a decision function [11] for supplier and buyer first. Seller’s agent responds at t_n to buyer’s agent offer sent at time t_n-1 and total profit is defined as Equation (1). Similarly, buyer’s agent also has a decision function to respond at t_n to seller’s agent offer sent at time t_n-1 and total cost is shown in Equation (2).

The buyer uses Equation (2) to decide whether he should accept suppliers’ proposal or not. Similarly, using a decision function Equation (1) a supplier can make decision.

・ Bilateral Negotiation model: If both rules based data and case based data are not matching with the user requirement, then Buyer’s agent and Seller’s agent will start the negotiation process with each other using Bilateral Negotiation model.

For negotiation process, we can use the Bilateral Negotiation model [12] .

Let x represents the buyer agent and y be the supplier agent. And let then i be the issues under negotiation, such as price, volume, duration, quality and so

on. Each agent assigns to each issue i a weight W_i, denoting the relative importance of that issue to the agent. Here, we consider quality as a weight. Hence, represents the importance of issue i to agent x, therefore the overall utility function of an offer O is

where the overall utility is denoted as U(O) for the offer, and u_i(x_i) is the individual utility function for issue i for u_i [0, 1] and the preference degree of an agent to an issue I is denoted as W_i [0, 9]. Each agent also specifies a minimum acceptable utility level [U_max, U_min] to determine if an offer is acceptable.

Hence, for benefit-oriented, the utility function U_i(x_i) is computed as follows :

For cost oriented however, the utility function can be as follows:

3) Feedback: When negotiation process was finished. Agent gives feedback to appropriate seller or buyer about negotiation whether it is successful or not.

We implemented E-negotiation agent system using rule based and case based approaches. We took a system (automated negotiation and behavior prediction) [6] as an existing system.

Mohammad Irfan Bala implemented an automated negotiation and behavior prediction system. In this system, during negotiation the agent will store the offers received and predict the preferences of the opponent based on these offers. The negotiation protocol determines the overall order of actions during a negotiation and the agents are obliged to stick to this protocol. In the bilateral alternating offers protocol two parties―agent A and agent B― take turns. Agent A starts the negotiation. Each turn an agent presents one of the three possible actions:

Accept: This action indicates that agent accepts the opponent’s last bid.

Offer: This action represents the bid made by an agent.

End negotiation: This action indicates that the agent terminates the negotiation.

We compared E-negotiation agent using rule based and case based approaches system with automated negotiation and Behavior Prediction system based on response time.

Table 1 shows the response time values of both systems. We calculated response time value of both the systems up to 300 users. Therefore we can say that, using rule based and case based approaches E-negotiation pro- cess requires less time as compared to existing system.

Figure 4 shows the comparison graph based on response time.

In E-negotiation agent using rule based and case based approaches system, all the data which is required for E-negotiation process such as product detail, rule base data, case base data is stored on cloud. Automated negotiation and Behavior Prediction system does not use Cloud computing concept to store negotiation process.

We calculated CPU utilization (in percentage) and Memory utilization (in percentage) of both systems up to 300 users.

Table 2 shows the Memory utilization values (in percentage) of both systems.

Therefore we can say that, due to use of Cloud computing E-negotiation agent using rule based and case based approaches system requires less CPU utilization and Memory utilization as compared to existing system.

Table 3 shows the CPU utilization values (in percentage) of both systems.

Figure 5 shows the comparison graph based on Memory utilization.

Figure 6 shows the comparison graph based on CPU utilization.

In order to make faster E-negotiation process, we use the rule base and case based approaches. Two or more parties are coming together during the negotiation process. And there are organizations to maintain data of the negotiation process and product data. But this maintenance is a very tedious job. In order to overcome this problem, all organizations’ product data are stored on cloud. So that system should become flexible.

This system is a Bilateral Negotiation model. In future this system can be implemented as multilateral negotiation model, behavior prediction and also uses the concept of expert system for increasing success rate of negotiation process.

Sheetal R.Vij,AmrutaMore,DebajyotiMukhopadhyay,Avinash J.Agrawal, (2015) An E-Negotiation Agent Using Rule Based and Case Based Approaches: A Comparative Study with Bilateral E-Negotiation with Prediction. Journal of Software Engineering and Applications,08,521-530. doi: 10.4236/jsea.2015.810049