KQML as an agent communication language
Software agents
XML, Java, and the future of the Web
World Wide Web Journal - Special issue on XML: principles, tools, and techniques
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
Monotonic mixing of decision strategies for agent-based bargaining
MATES'11 Proceedings of the 9th German conference on Multiagent system technologies
Modelling partner’s behaviour in agent negotiation
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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Agent technology has been applied to the Electronic Commerce domain, giving birth to what is known as agent-mediated Electronic Commerce. Current real-world applications refer only to the delegation of product or merchant brokering tasks to software agents. Automated negotiation is a less explored stage in this field, since it implies the trust of bargaining power to software agents. We here present SMACE, a layered platform for agentmediated Electronic Commerce, supporting multilateral and multiissue automated negotiations. In this system, the negotiation infrastructure through which the software agents interact is independent from their negotiation strategies. SMACE has been used to test several negotiation strategies. The system includes agents that are capable of increasing their performance with their own experience, by adapting to the market conditions. This adaptation is reached through the use of Reinforcement Learning techniques. In order to test the agents' adaptation process, several different experiments have been tried out, and the respective results are here reported. These results allow us to conclude that it is possible to build negotiation strategies that can outperform others in some environments. In fact, knowledge gathered about past negotiations can be a strategic advantage in some scenarios.