{'fraction_non_alphanumeric': 0.016074649102172038, 'fraction_numerical': 0.0010977809142946758, 'mean_word_length': 4.596314172882844, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Agent Based Trust Management Model Based on Weight Value Model for Online Auctions

This paper is aimed at the stipulations which arise in the traditional online auctions as a result of various anomalies in the reputation and trust calculation mechanism. We try to improve the scalability and efficiency of the online auctions by providing efficient trust management methodology considering several factors into consideration. A comparison between the performance of the auctions system with and without the agent methodology is done with good results


Introduction
An online auction is simply defined as a virtual marketplace hosted on the Internet to match buyers and sellers of goods around the globe regardless of the physical limitations of traditional auctions such as geography, presence, time, and space. Online auctions operate different protocols including English, Dutch, First-Price Sealed Bid and Vickery with different properties for each one of these protocols. Here in this system we implement the different auctions and also help the users to decide upon the optimal price of the product considering a specific set of parameters. Our main interest would be to work on the reputation and trustworthiness of the participants in the online auctions.
Since the participants in the auctions do not know the details of the seller, they have no other option rather than to trust the feedback mechanisms provided implicitly. This leaves a lot of scope for improvement in this field regarding the exact portrayal of real time scenarios of online auctions.
There is also the problem of the need for constant human monitoring.
This paper is organised as follows. The Section II deals with previously achieved progress and contemporary work done in the field of e-commerce with emphasis on online auctions. In Section III, the framework of the system is dealt with. In section IV, the agent technology is explained in detail. In the fifth section, trust calculation using the proposed formula is done. In the sixth section, the results and conclusions to depict the scalability and efficiency of the system are shown.

Related work
The An example of how the trustworthiness is calculated on most of the auction sites is as follows. They use a feedback system, which allows users of the online auction system to rate sellers and buyers based on the outcome of the transactions.  The colors of the stars can be yellow, blue, Turquoise, purple, red, green, and several others. This is decided based on the amount of points obtained. Based on this system, a user can decided on whom to do business with by looking up a person's feedback. A user that has a majority of positive feedback will do more business than a user that has a majority of negative feedback. These records however, are stored on a central server that is maintained by the organization. The present day auction sites mostly require the users to constantly monitor the proceedings of the auction in which they are involved. This can be a rather tiresome business.
To avoid this problem we use concept of agent technology. A comparison between the performance of the auctions system with and without the agent methodology is done with agents. This comparison is based on several factors such as the time taken for the auction, the original price obtained versus the price expected etc.

Framework
The frame work consists of various components like Auction agents, Trust management agent. An agent is defined as a software entity that can perform information-related tasks without ongoing human supervision. This methodology is achieved by the usage of JADE agent technology.
The auction agent deals with the implementation of the type of auction specified. It also deals with the support provided to the user regarding the optimal price taking the parameters of quantity and priority of sales into account. The auction agent takes responsibility for the implementation of the negotiation autonomously. It takes the amount as input from the user and uses this as a threshold and bids on behalf of the user and thus the user need not monitor the proceedings at all times. The auction agent is unbiased in its functioning and strives to provide the best results to the entire clientele population. The auction agents are spawned separately for each new user arriving to participate in the auction based on their customized preferences.
The trust management agent is used to calculate the reputation of all the users taking part in the auctions either directly or indirectly. The reputation levels depict the trustworthiness of the person who is hosting the product. By trustworthiness, we mean that we are finding the weight value of the rater or user. This can be done on the basis of several factors like feedback decay, recent price, rater's trustworthiness etc. In feedback rating the rater generally rates the host according to several critical attributes which may be the quality of service provided, the type of technical support provided, the delivery of the product, the item's condition on delivery etc. Thus feedback rating as considered in the present methodologies is not a scalar but a vector quantity. Thus it can lie in the closed interval of -1 to 1 and not strictly one of the extremes.
We must also take into consideration the other factors of influence such as the trust

Agent Technology
Agent technology is used for the task of automating e-commerce business processes in view of bringing efficiency, scalability and profitability to businesses and individual users.
Agent technologies can be used to depict the real world scenarios in the field of e-commerce onto the virtual screen.

Trust Management
The trust calculation is done based on the reputation of the person who is rating. Consider 'x' as the person whose weight value is needed for the trustworthiness. Let 'y' be another user who has a common partner set with 'x'. E.g. the common partner set means that suppose x and y are clients who have won auctions hosted by some common set of people. The weight value of the trust worthiness of x is calculated by comparing the ratings given by x to each and every element of the common set as compared to the ratings given to the same element by y. Several critical attributes are taken into consideration while the rating procedure is done. Here, • f cr(i) (x,a) denotes the rating awarded by x to a for the critical attribute of i.
• N(common(x,y)) denotes the number of users common to x and y.
• n(days) denotes the number of days in which the auction should be complete.
• f(n-1 auction) is the feedback the person had until the last auction he hosted was complete.
• Exp is the experience value. The experience values are calculated based on the number of auctions participated and the number of auctions won.
Suppose that x is a client in an e auction scenario. This implies that x is a potential candidate for winning the auction and hence a potential rater. Suppose that x has won the auctions hosted by a, b, c and d. according to the methodology described above, another client who has won auctions which form a subset of that of x is found. By using these values which are stored in the database for the purpose of comparison, the weight value of x is found using the first equation. Thus the numerator in the first equation turns into the following.
The above calculation is continued for each and every element of the set N common(x, y) which are b, c and d . Here f (c1(x, a)) denotes the value 3.5 in the table 2 which means the feedback given by x to a for critical attribute of C 1. If the above result is considered a R a and the corresponding results for b, c and d are R b, R c, and R d respectively . Thus the weight value of x is calculated as follows   The critical attributes mainly revolve around the extent of customer satisfaction, the quality of the product when received as compared to that of promised quality by the vendor etc.
The calculation of optimal price is required to further enhance the accuracy in the prediction of the trust values. The optimal price calculated is assigned some weight value which is based on the experience of the vendors or the hosts of the auctions. The effect of the optimal price on the trustworthiness is solely dependent on the type of tangible goods sold and the type of services provided and the type of customers who visit it.
The optimal price is calculated taking the data given by the seller. • Security through 3rd party: Globally recognized security must be used.
• Added Incentives: Trust can be built easily if the customer thinks that he has nothing to lose. So giving advices regarding what else did the other customer buy with this product, and information regarding the offers available and efficient trust model help the cause.
• Experience: Feedback facility should be provided to share the experiences as previous experiences which can be self or transferred play a major role in trust management.

Results
The system is successful in catering to the needs of the users by providing them the automated and less tiresome more reliable way of online shopping. The performance, scalability and efficiency of the system discussed is more compared to previous models The comparison between the performances of various auction protocols has been done in two cases. One with agents and the other without agents is considered. The plain line indicates the one with agents, the bar chart represents the expected price and the line with markers represents the one without agents. Fig.7 shows the performance when the price is taken into consideration as a key factor.
We take the expected price from the optimal price formula and compare it with the final prices obtained in the system with agents and also in the one without agents. It depicts that in most cases the price at which the buyer bought the product is often less in the system which uses agent technology. This is because of the fact that there is a continuous monitoring on the auction participated by the buyer using the buyer agent and thus the negotiation strategies of the buyer are much more effective than the traditional online auctions where the buyer has to manually keep track of all the proceedings of the auction in real time.
The Fig 8 shows that the performance of the auctions with agents is much better than the one without agent technology in case of dutch protocol. The price is often marginally low in case of the auctions system which use agent technology. Unlike the english auction scenario, the difference in performance is quite evident in the dutch auction scenario.
In the dutch auction scenario the result is very evident because here we provide a range of values where a buyer can stop the negotiation process and purchase the product. But in the normal phase if the buyer is not present at the exact time at which the seller reduces the price in his favor, then some other buyer will buy the object. by using agents there is no scope of such mistakes happening.
The Fig 9 shows the variation in case of Vickrey auction scenario. The price is almost the same in both the cases with and without agents except a few minor marginal changes. But the variation in optimal price is very high and is rather unpredictable based on the graph. There are no major changes in both the systems except when the price range is at the extremeities. Thus the performance in the case of vickery auctions taking the price factor into consideration can be highly unpredictable as they belong to the category of sealed bid auctions. Thus the most Fig.7 Price variation in English auction with and without agents.
effective performance in many cases has been found to be in the systems which use the agent technology. The working of the optimal price which has been proposed has been most effective in case of the English auction scenario. Fig.9 Price variation in vickrey auction with and wihtout agents.

Conclusion and Future work
Work is done on the task of increasing the reliability of these feedback mechanisms taking certain parameters into consideration. The impact of malicious feedbacks is reduced.
There are many other real time factors which are necessary to be taken into consideration to depict the dynamic nature of the present world scenario of e-auctions. To generalize the agents to participate in other types of auctions such as stock exchanges, which are asynchronous double auctions would be a good future prospective. Another future enhancement will be to add additional bidding strategies. Detailed performance statistics will be collected to determine which strategies perform better under which types of auctions. Comparison of these strategies with the human strategies is also an option.