Approximation capabilities of multilayer feedforward networks
Neural Networks
Neural Computation
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Communications of the ACM
WWW-based negotiation support: design, implementation, and use
Decision Support Systems
A Real-Life Experiment in Creating an Agent Marketplace
Software Agents and Soft Computing: Towards Enhancing Machine Intelligence, Concepts and Applications
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
Intelligent agents for supporting construction procurement negotiation
Expert Systems with Applications: An International Journal
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Affective negotiation support systems
Journal of Ambient Intelligence and Smart Environments
Pairwise issue modeling for negotiation counteroffer prediction using neural networks
Decision Support Systems
A new mechanism for negotiations in multi-agent systems based on ARTMAP artificial neural network
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
A negotiation based approach for service composition
DESRIST'10 Proceedings of the 5th international conference on Global Perspectives on Design Science Research
DESRIST'10 Proceedings of the 5th international conference on Global Perspectives on Design Science Research
The learning of an opponent's approximate preferences in bilateral automated negotiation
Journal of Theoretical and Applied Electronic Commerce Research
Computers and Industrial Engineering
A novel strategy for efficient negotiation in complex environments
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
Expert Systems with Applications: An International Journal
A neural network approach to predicting price negotiation outcomes in business-to-business contexts
Expert Systems with Applications: An International Journal
Optimizing complex automated negotiation using sparse pseudo-input gaussian processes
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
An efficient automated negotiation strategy for complex environments
Engineering Applications of Artificial Intelligence
Conditional restricted Boltzmann machines for negotiations in highly competitive and complex domains
IJCAI'13 Proceedings of the Twenty-Third international joint conference on 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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Electronic negotiation experiments provide a rich source of information about relationships between the negotiators, their individual actions, and the negotiation dynamics. This information can be effectively utilized by intelligent agents equipped with adaptive capabilities to learn from past negotiations and assist in selecting appropriate negotiation tactics. This paper presents an approach to modeling the negotiation process in a time-series fashion using artificial neural network. In essence, the network uses information about past offers and the current proposed offer to simulate expected counter-offers. On the basis of the model's prediction, ''what-if'' analysis of counter-offers can be done with the purpose of optimizing the current offer. The neural network has been trained using the Levenberg-Marquardt algorithm with Bayesian Regularization. The simulation of the predictive model on a testing set has very good and highly significant performance. The findings suggest that machine learning techniques may find useful applications in the context of electronic negotiations. These techniques can be effectively incorporated in an intelligent agent that can sense the environment and assist negotiators by providing predictive information, and possibly automating some negotiation steps.