A semantics approach for KQML—a general purpose communication language for software agents
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Reaching agreements through argumentation: a logical model and implementation
Artificial Intelligence
Machine learning and knowledge representation in the LaboUr approach to user modeling
UM '99 Proceedings of the seventh international conference on User modeling
Multi-agent Infrastructure, Agent Discovery, Middle Agents for Web Services and Interoperation
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
Agents modeling agents in information economies
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Conversation mining in multi-agent systems
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
Multi-agent based classification using argumentation from experience
Autonomous Agents and Multi-Agent Systems
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The paper presents a multi-agent system that comprises a society of self-interested agents that use argumentation-based negotiation to reach agreements regarding cooperation and goal satisfaction. The system is a generalization of some argumentation-based multi-agent systems proposed in the literature in which better cooperation agreements are reached through the use of human-like arguments. We then show how this type of negotiation can be adapted according to evolved models of other agents in the system. Negotiation is performed using different types of arguments varying from quantitative ones, such as money or trade objects, to qualitative arguments, such as promises, appeal to past promises, and past examples. The models of the other agents are built and refined incrementally during negotiation; these models are then used to adapt the negotiation strategy according to other agents' desires, preferences and behavioral characteristics during interactions.