Applying hybrid case-based reasoning in agent-based negotiations for supply chain management
Expert Systems with Applications: An International Journal
Pairwise issue modeling for negotiation counteroffer prediction using neural networks
Decision Support Systems
A novel strategy for efficient negotiation in complex environments
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
An ontology based approach to organize multi-agent assisted supply chain negotiations
Computers and Industrial Engineering
An efficient automated negotiation strategy for complex environments
Engineering Applications of Artificial Intelligence
A utility concession curve data fitting model for quantitative analysis of negotiation styles
Expert Systems with Applications: An International Journal
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This paper presents a learning mechanism that applies nonlinear regression analysis to predict a negotiation agent's behaviour based only the opponent's previous offers. The behaviour of negotiation agents in this study is determined by their tactics in the form of decision functions. Heuristics based on estimates of an agentýs tactics are drawn from a series of experiments. The findings of this empirical study show that this approach can be used to obtain better deals than existing decision function tactics. The learning mechanism can be used online, without any prior knowledge about other agents and is therefore, very useful in open systems where agents have little or no information about each other.