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(U Waterloo, Dept of Comput- er Science, Waterloo, ON, Canada) A reputation-oriented reinforce- ment learning strategy for agents in electronic marketplaces. ... —Proposes a reputation-oriented reinforcement learning algorithm for buying and selling agents in electronic market environments. ...
In this paper, we propose a reputation oriented reinforcement learning algorithm for buying and selling agents in electronic market environments. ... We also include the ability for buying agents to optionally explore the marketplace in order to discover new reputable sellers. ... In this paper, we propose a reinforcement learning and reputation based algorithm for buying and selling agents in electronic market environments. ...doi:10.1111/1467-8640.t01-1-00203 fatcat:xe4e2g2z2vddpdwfewevs4uzxy
In this paper, we propose a marketplace model which is based on personality, reputation and reinforcement learning algorithms for buying and selling agents. ... In addition, seller agents apply reinforcement learning to establish a model of reputation of buyer agents. ... In developing learning algorithms for agents in electronic marketplaces, we use a reputation mechanism, in addition to reinforcement learning, to provide added robustness to buying agents. ...doi:10.5897/ajbm11.2492 fatcat:2edwjacysrcefhjhq3edqcazxq
reinforcement learning strategy for agents in electronic marketplaces. ... Summary: “In this paper, we propose a reputation-oriented re- inforcement learning algorithm for buying and selling agents in electronic market environments. ...
In this article, we present an approach for the design of adaptive business agents that uses a combination of reinforcement learning and reputation modeling. ... —Adaptive business agents op- erate in electronic marketplaces, learning from past experiences to make ef- fective decisions on behalf of their users. ...
The unfair rating problem exists when a buying agent models the trustworthiness of selling agents by also relying on ratings of the sellers from other buyers in electronic marketplaces, that is in a reputation ... More importantly, in this work, we focus on experimental comparison of our approach with two key models in a simulated dynamic e-marketplace environment. ... Reinforcement Learning Model Tran and Cohen  have buying agents use reinforcement learning to determine with which selling agents to do business, in order to maximize the buyers' expected profit. ...doi:10.4067/s0718-18762011000300005 fatcat:hjvgyk75tzg75o5psnwgfxhuwq
For example, a variation in the delivered Quality of Services (QoS) can frustrate buyers and they leave the e-marketplace, causing revenue loss. ... The performance of e-marketplaces plays a crucial role in attracting and retaining buyers. ... Consumers adopt reinforcement learning approach to model the trustworthiness of super-agents. ...doi:10.1145/2631775.2631821 dblp:conf/ht/NoorianMV14 fatcat:rxllxwcxsncu5ffxtentwmpmgu
Communications in Computer and Information Science
This paper proposes a dynamic reputation framework using reinforcement learning and fuzzy set theory that ensures judicious use of information sharing for inter-agent cooperation. ... A dynamic reputation model would provide virtually instantaneous knowledge about the changing e-market environment and would utilise Internets' capacity for continuous interactivity for reputation computation ... Phase II includes reputation computation using reinforcement learning. ...doi:10.1007/978-3-642-22714-1_30 fatcat:x3fmc5tjtbahpa6sjko5dkz3xa
Based on the implication of such analysis, we then focus on experimental comparison of our approach with two key models in a framework that simulates a dynamic electronic marketplace environment. ... Our study provides the basis for deciding which approach is most appropriate to employ, in which scenario. ... Introducing innovation to the design of trust modeling systems used in agent-oriented e-marketplaces is a crucial concern, as part of the ongoing effort to promote electronic commerce to businesses and ...doi:10.1109/pst.2008.16 dblp:conf/pst/ZhangSC08 fatcat:r5hfc64f3nefhce6doggsrhkla
This paper proposes a dynamic reputation framework using reinforcement learning and fuzzy set theory that ensures judicious use of information sharing for inter-agent cooperation. ... A dynamic reputation model would provide virtually instantaneous knowledge about the changing e-market environment and would utilise Internets' capacity for continuous interactivity for reputation computation ... Phase II includes reputation computation using reinforcement learning. ...arXiv:1110.3961v1 fatcat:7nitzirkufcg5dv6yz7dztgrne
sharing for inter-agent cooperation and also associates the reputation of an agent with the value of a transaction so that the market approaches an equilibrium state and dishonest agents are weeded out ... This paper evaluates various reputation systems for assigning reputation rating to software agents acting on behalf of buyers and sellers in e-market. ... Various reputation models [1, 15] described in the literature are based on reinforcement learning. ...arXiv:1303.7377v1 fatcat:vr3qmc4ywrfoxppxovw4bjvx6a
This paper provides a dynamic approach to compute the aggregated shared reputation component by filtering out unfair advice and then generating the aggregated shared reputation value. ... Reputation systems aim to reduce the risk of loss due to untrustworthy participants. This loss is aggravated by dishonest advisors trying to pollute the e-market environment for their self-interest. ... This paper utilises Reinforcement learning (RL) for modeling the reputation of advisors. ...arXiv:1201.3835v1 fatcat:ciyk6sgwovd2fdo7wj2eto7y5y
Understanding Complex Systems
Abstract In this chapter, the role of reputation as a distributed instrument for social order is addressed. ... A short review of the state of the art will show the role of reputation in promoting (a) social control in cooperative contexts -like social groups and subgroups -and (b) partner selection in competitive ... A conference aiming to propose a scientific approach to Reputation has been organized in 2009: the first International Conference on Reputation, ICORE 2009. ...doi:10.1007/978-3-540-93813-2_15 fatcat:whw6daaeprhp3p3u4md6w7dqme
In the context of e-commerce, it is critical for a retailing com pany to be able to assess the market and respond quickly to changes in competition and/or service levels and availability of its products ... offer and operates internationally in a number of market places. ...  on using reinforcement learning techniques to improve dynamic pricing agents' performance, where they show the effect on the marketplaces and the effect of different adaptive pricing strategies ...doi:10.1145/2346536.2346538 dblp:conf/ACMicec/ElakehalP12 fatcat:bwh7qsilanayjfmco3j32y4l4e
2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference
of a single company. ... The process of negotiation and contracting of complex services that can be provided in a cooperative manner by member companies of the ecosystem is an essential binding element among them. ... It will explore possible synergies between the reputation techniques and the learning and recommendation approaches used in ONE. ...doi:10.1109/dest.2007.371968 fatcat:3eoc3ucfcrainath4cs7dwitfa
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