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Electronic markets are places where entities not known in advance can negotiate and agree upon the exchange of products. Intelligent agents can be proved very advantageous when representing entities in markets. Mostly, such entities are based on reputation models in order to conclude a transaction. However, reputation is not the only parameter that they could be based on. In this work, we deal with...
In electronic marketplaces automated and dynamic pricing is becoming increasingly popular. Agents that perform this task can improve themselves by learning from past observations, possibly using reinforcement learning techniques. several papers studied the use of Q-learning for modeling the problem of dynamic pricing in electronic marketplaces. But The extension of reinforcement learning (RL) to large...
Information technology has given e-retailers new capability of learning demand in real time. This paper investigates how to integrate this real time learning technology with Q-learning algorithm for the optimization of dynamic pricing in e-retailing setting. Especially, this paper studies the optimal dynamic pricing problem for seasonal and style products in e-retailing setting, and validate our approach...
Online auctions have become extremely popular in recent years. Ability to predict winning bid prices accurately can help bidders to maximize their profit. This paper proposes a number of strategies and algorithms for performing such predictions for the first price sealed bid reverse auctions (FPSBRA). The neural networks (NN) and genetic programming (GP) learning techniques are used in the models...
With the rapid development of multi-agent based E-commerce systems, on-line automatic negotiation protocol is often needed. But because of incomplete information agents have, the efficiency of on-line negotiation protocol is rather low. To overcome the problem, an on-line agent bilateral multi-issue alternate bidding negotiation protocol based on reinforcement learning is present. The reinforcement...
Dynamic pricing in electronic marketplaces is a basic problem in electronic commercial. In multiagent environments, the optimal pricing policy of agent depends on the pricing policies of other agents. This makes the learning problem more problematic. This paper proposes an efficient online learning algorithm, which integrates the observed objective actions as well as the subjective inferential intention...
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