10574398
A reputation-oriented reinforcement learning approach for agents in electronic marketplaces
The problem of how to design personal, intelligent agents for e-commerce applications is a subject of increasing interest from both the academic and industrial research communities. In our research, we consider the agent environment as an open marketplace which is populated with economic agents (buyers and sellers), freely entering or leaving the market. The problem we are addressing is how best to model the electronic marketplace, and what kinds of learning strategies should be provided, in order to improve the performance of buyers and sellers in electronic exchanges. Our strategy is to introduce a reputation-oriented reinforcement learning algorithm for buyers and sellers. We take into account the fact that multiple sellers may offer the same good with different qualities. In our approach, buyers learn to maximize their expected value of goods and to avoid the risk of purchasing low quality goods by dynamically maintaining sets of reputable sellers. Sellers learn to maximize their expected profits by adjusting product prices and by optionally altering the quality of their goods. In our buying algorithm, a buyer b uses an expected value function f b , where f b (g, p,s) represents buyer b’s expected value of buying good g at price p from seller s. Buyer b maintains reputation ratings for sellers, and chooses among its set of reputable sellers S b
12269477
An Intelligent Agent-Based Framework for Mobile Business
This paper proposes a mobile, intelligent agent-based framework that allows buyers and sellers to perform business at remote locations. A business participant can generate a mobile, intelligent agent via a mobile device and dispatch the agent to the Internet to do business. The proposed framework brings in several advantages: First, it provides great convenience for traders as business can be conducted anytime and anywhere. Secondly, the user is freed from the time-consuming task of finding and negotiating with appropriate traders. Thirdly, it addresses the problem of limited and expensive connection time for mobile users: A trader can disconnect her mobile device from its server after launching a mobile agent. Later on, she can reconnect and call back the agent for results, therefore minimizing the connection time. Finally, by complying with the standardization body of FIPA, this flexible framework increases the interoperability between agent systems and provides high design scalability.
185808
Equilibrium of E-markets populated with reputation oriented learning agents
In this paper, we propose reputation oriented reinforcement learning algorithms for buying and selling agents in electronic marketplaces. We consider the fact that the quality of a good offered by multiple selling agents may not be the same, and a selling agent may alter the quality of its goods. In our approach, buying agents learn to avoid the risk of purchasing low quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable and disreputable sellers. Selling agents learn to maximize their expected profits by adjusting product prices and optionally altering the quality of their goods. This paper focusses on presenting results from experiments investigating the behaviour of an e-market populated with our buying and selling agents. Our results show that such a market can reach an equilibrium state where the agent population remains stable, and this equilibrium is optimal for the participant agents.
14131447
A Reputation-Oriented Reinforcement Learning Strategy for Agents in Electronic Marketplaces
In this paper, we propose a reputation–oriented reinforcement learning algorithm for buying and selling agents in electronic market environments. We take into account the fact that multiple selling agents may offer the same good with different qualities. In our approach, buying agents learn to avoid the risk of purchasing low–quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable sellers. Selling agents learn to maximize their expected profits by adjusting product prices and by optionally altering the quality of their goods. Modeling the reputation of sellers allows buying agents to focus on those sellers with whom a certain degree of trust has been established. We also include the ability for buying agents to optionally explore the marketplace in order to discover new reputable sellers. As detailed in the paper, we believe that our proposed strategy leads to improved satisfaction for buyers and sellers, reduced communication load, and robust systems. In addition, we present preliminary experimental results that confirm some potential advantages of the proposed algorithm, and outline planned future experimentation to continue the evaluation of the model.
17878817
Designing recommender systems for e-commerce: an integration approach
In electronic commerce applications, prospective buyers may be interested in receiving recommendations to assist with their purchasing decisions. Previous research has described two main models for automated recommender systems: collaborative filtering and knowledge-based approaches. In this paper, we present an architecture for designing a hybrid recommender system that combines these two approaches. We then discuss how such a recommender system can switch between the two methods, depending on the current support for providing good recommendations from the behavior of other users, required for the collaborative filtering option. We also comment on how the overall design is useful to support recommendations for a variety of product areas and present some directions for future work.
31299466
The Advantages of Designing Adaptive Business Agents Using Reputation Modeling Compared to the Approach of Recursive Modeling
Adaptive business agents operate in electronic marketplaces, learning from past experiences to make effective decisions on behalf of their users. How best to design these agents is an open question. In this article, we present an approach for the design of adaptive business agents that uses a combination of reinforcement learning and reputation modeling. In particular, we take into account the fact that multiple selling agents may offer the same good with different qualities, and that selling agents may alter the quality of their goods. We also consider the possibility of dishonest agents in the marketplace. Our buying agents exploit the reputation of selling agents to avoid interaction with the disreputable ones, and therefore to reduce the risk of purchasing low value goods. We then experimentally compare the performance of our agents with those designed using a recursive modeling approach. We are able to show that agents designed according to our algorithms achieve better performance in terms of satisfaction and computational time and as such are well suited for the design of electronic marketplaces.
14505964
Design and Implementation of Davis Social Links OSN Kernel
Social network popularity continues to rise as they broaden out to more users. Hidden away within these social networks is a valuable set of data that outlines everyone's relationships. Networks have created APIs such as the Facebook Development Platform and OpenSocial that allow developers to create applications that can leverage user information. However, at the current stage, the social network support for these new applications is fairly limited in its functionality. Most, if not all, of the existing internet applications such as email, BitTorrent, and Skype cannot benefit from the valuable social network among their own users. In this paper, we present an architecture that couples two different communication layers together: the end2end communication layer and the social context layer, under the Davis Social Links (DSL) project. Our proposed architecture attempts to preserve the original application semantics (i.e., we can use Thunderbird or Outlook, unmodified, to read our SMTP emails) and provides the communicating parties (email sender and receivers) a social context for control and management. For instance, the receiver can set trust policy rules based on the social context between the pair, to determine how a particular email in question should be prioritized for delivery to the SMTP layer. Furthermore, as our architecture includes two coupling layers, it is then possible, as an option, to shift some of the services from the original applications into the social context layer. In the context of email, for example, our architecture allows users to choose operations, such as reply, reply-all, and forward, to be realized in either the application layer or the social network layer. And, the realization of these operations under the social network layer offers powerful features unavailable in the original applications. To validate our coupling architecture, we have implemented a DSL kernel prototype as a Facebook application called CyrusDSL (currently about 40 local users) and a simple communication application combined into the DSL kernel but is unaware of Facebook's API.