Reaching agreements through argumentation: a logical model and implementation
Artificial Intelligence
Bayesian reasoning in an abductive mechanism for argument generation and analysis
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Introduction to Bayesian Networks
Introduction to Bayesian Networks
A Framework for Argumentation-Based Negotiation
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Towards interest-based negotiation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Argumentation based decision making for autonomous agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A Generic Framework for Argumentation-Based Negotiation
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Argumentation in bayesian belief networks
ArgMAS'04 Proceedings of the First international conference on Argumentation in Multi-Agent Systems
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Negotiation is one of the most fundamental and effective mechanism for resolving conflicts between self-interested agents and producing mutually acceptable compromises. Most existing research in negotiation presumes a fixed negotiation context which cannot be changed during the process of negotiation and that the agents have complete and correct knowledge about all aspects of the issues being negotiated. In practice, the issues being negotiated may change and the agents may have incorrect beliefs of relevant issues updated during the negotiation process. Argumentation-based negotiation approaches have therefore been proposed to capture such realistic negotiation contexts. Here we present a novel Bayesian network based argumentation and decision making framework that allows agents to utilize models of the other agents. The agents will generate effective arguments to influence the other agent’s belief and produce more profit.