Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
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Proceedings of the 1st ACM conference on Electronic commerce
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Computer Networks: The International Journal of Computer and Telecommunications Networking - electronic commerce
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AMET '98 Selected Papers from the First International Workshop on Agent Mediated Electronic Trading on Agent Mediated Electronic Commerce
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HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
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ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
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ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
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The Knowledge Engineering Review
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WOEC'95 Proceedings of the 1st conference on USENIX Workshop on Electronic Commerce - Volume 1
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Implementing rule-based mechanisms for agent-based price negotiations
Proceedings of the 2006 ACM symposium on Applied computing
EATIS '07 Proceedings of the 2007 Euro American conference on Telematics and information systems
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KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
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Computers and Industrial Engineering
Rule-Based automated price negotiation: overview and experiment
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Rule-Based framework for automated negotiation: initial implementation
RuleML'05 Proceedings of the First international conference on Rules and Rule Markup Languages for the Semantic Web
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One problem with existing agent-mediated negotiation systems is that they rely on ad hoc, static, non-adaptive, and hardcoded schemes to represent the behavior of agents. This limitation is probably due to the complexity of the negotiation task itself. Indeed, while negotiating, software (human) agents face tough decisions. These decisions are based not only on the information made available by the negotiation server, but on the behavior of the other participants in the negotiation process as well. The information and the behavior in question are constantly changing and highly uncertain. In this paper, we propose a rule-driven approach to represent, manage and explore negotiation strategies and coordination information. Among the many advantages of this solution, we can cite the high level of abstraction, the closeness to human understanding, the versatility, and the possibility to modify the agents' behavior during the negotiation. To validate our approach, we ran several agent tournaments, and used a rule-driven mechanism to implement bidding strategies that are common in the English and Dutch auctions. We also implemented simple coordination schemes across several auctions. The ongoing validation work is detailed and discussed in the second part of the paper.