Supporting the negotiation life cycle
Communications of the ACM
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Intelligent agents for automated one-to-many e-commerce negotiation
ACSC '02 Proceedings of the twenty-fifth Australasian conference on Computer science - Volume 4
Bargaining on an Internet Agent-based Market: Behavioral vs. Optimizing Agents
Electronic Commerce Research
A personalized and integrative comparison-shopping engine and its applications
Decision Support Systems - Special issue: Agents and e-commerce business models
On Constraint-Based Reasoning in e-Negotiation Agents
Agent-Mediated Electronic Commerce III, Current Issues in Agent-Based Electronic Commerce Systems (includes revised papers from AMEC 2000 Workshop)
On Fuzzy e-Negotiation Agents: Autonomous Negotiation with Incomplete and Imprecise Information
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Genetic Algorithms for Automated Negotiations: A FSM-Based Application Approach
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Determining Successful Negotiation Strategies: An Evolutionary Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Automated e-business negotiation: model, life cycle, and system architecture
Automated e-business negotiation: model, life cycle, and system architecture
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Predicting Agents Tactics in Automated Negotiation
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Learning opponents' preferences in multi-object automated negotiation
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
An evolutionary learning approach for adaptive negotiation agents: Research Articles
International Journal of Intelligent Systems - Learning Approaches for Negotiation Agents and Automated Negotiation
International Journal of Human-Computer Studies
A machine-learning approach to automated negotiation and prospects for electronic commerce
Journal of Management Information Systems - Special issue: Information technology and its organizational impact
Intelligent agents for e-marketplace: negotiation with issue trade-offs by fuzzy inference systems
Decision Support Systems
Managing commitments in multiple concurrent negotiations
Electronic Commerce Research and Applications
A model for multi-lateral negotiations on an agent-based job marketplace
Electronic Commerce Research and Applications
Learning user preferences for multi-attribute negotiation: an evolutionary approach
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
Expert Systems with Applications: An International Journal
Swarm intelligence supported e-remanufacturing
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
| Hi-index | 12.05 |
In recent years, enormous research effort has been spent on the development of multi-agent systems to automate buyer-seller negotiations in supply chain management (SCM) applications. In many of these agent-based negotiation systems, the negotiation process is restricted to the stage when agents interact to exchange bargaining offers; activities in the pre-bargaining and post-bargaining stages are ignored. With a more comprehensive perspective, the negotiation lifecycle comprises a number of phases including the pre- and post-bargaining phases, in addition to the phase when participants interact to bargain. In this paper, a hybrid case-based reasoning approach is applied in the pre- and post-negotiation phases to support adaptive negotiation strategy for buyer-seller negotiations in SCM applications. When a new negotiation problem arrives, a similar previous negotiation case is retrieved from the case database and its solutions are recommended for inferring the negotiation parameters. The recommended parameters have to be adjusted to suit the new negotiation problem. Subsequent to negotiation completion, negotiation parameters and the negotiation outcome are analyzed and evaluated. After the evaluation, the new negotiation case, including the problem descriptions and solutions, is retained in the case database for future applications.