Multiagent compromise via negotiation
Distributed Artificial Intelligence (Vol. 2)
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Reaching agreements through argumentation: a logical model and implementation
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Determining Successful Negotiation Strategies: An Evolutionary Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
On Agent-Mediated Electronic Commerce
IEEE Transactions on Knowledge and Data Engineering
Belief Revision for Adaptive Negotiation Agents
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
BT Technology Journal
An agenda-based framework for multi-issue negotiation
Artificial Intelligence
Agent-mediated electronic commerce: a survey
The Knowledge Engineering Review
Towards Genetically Optimised Responsive Negotiation Agents
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Agent behaviors in virtual negotiation environments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A weighted sum genetic algorithm to support multiple-partymultiple-objective negotiations
IEEE Transactions on Evolutionary Computation
Towards a web services and intelligent agents-based negotiation system for B2B eCommerce
Electronic Commerce Research and Applications
LEARNING DRIFTING NEGOTIATIONS
Applied Artificial Intelligence
A social approach for learning agents
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Negotiation has been identified as one of the key steps in Business-to-Business (B2B) transaction models. However, developing effective and efficient negotiation mechanisms for e-Business is quite challenging since negotiations in such a context are characterized by combinatorial complex negotiation spaces, tough deadlines, incomplete information about the opponents, and volatile negotiator preferences. Classical negotiation models are not able to offer a satisfactory solution to address all these issues. This paper illustrates our adaptive negotiation agents which are underpinned by a robust evolutionary learning mechanism to deal with complex and dynamic negotiation situations often encountered in e-Business applications. Our experimental results show that the proposed evolutionary negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for e-Business.