KQML as an agent communication language
Software agents
The Michigan Internet AuctionBot: a configurable auction server for human and software agents
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Doing Business Electronically: A Global Perspective of Electronic Commerce
Doing Business Electronically: A Global Perspective of Electronic Commerce
How Can an Agent Learn to Negotiate?
ECAI '96 Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages
Determining Successful Negotiation Strategies: An Evolutionary Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Agent-mediated electronic commerce: a survey
The Knowledge Engineering Review
Agents' Advanced Features for Negotiation and Coordination
EASSS '01 Selected Tutorial Papers from the 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School on Multi-Agent Systems and Applications
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
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Market research suggests that organisations, in general, have a differentiation strategy when approaching Electronic Commerce. Thus, in order to be useful, agent technology must take into account this market characteristic. When extending its application to the negotiation stage of the shopping experience, one should consider a multi-issue approach, from which both buyers and sellers can profit. We here present SMACE, a layered platform for agent-mediated Electronic Commerce, supporting multilateral and multiissue negotiations. In this system, the negotiation infrastructure through which the software agents interact is independent from their negotiation strategies. Taking advantage of this concept, the system assists agent construction, allowing users to focus in the development of their negotiation strategies. In particular, and according to experiments here reported, we have implemented a type of agent that is capable of increasing the performance with its own experience, by adapting to the market conditions, using a specific kind of Reinforcement Learning technique.