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
Agent behaviors in virtual negotiation environments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Predicting opponent's moves in electronic negotiations using neural networks
Expert Systems with Applications: An International Journal
Argumentation-based agents for eProcurement
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track
Selecting an appropriate excavation construction method based on qualitative assessments
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Designing multi-agent systems: a framework and application
Expert Systems with Applications: An International Journal
A multi-agent based system for e-procurement exception management
Knowledge-Based Systems
Agent-based decision making in the electronic marketplace: interactive negotiation
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Hi-index | 12.06 |
Negotiation is commonly required to reach a final contractual agreement in construction material procurement. However, even simple negotiations often result in suboptimal agreements, thus 'leaving money on the table.' An automated system that could evaluate bids, negotiate to finalize the bid, and value the individual characteristics of negotiating parties would be useful to both contractors and suppliers. This study examines common negotiable issues and options for construction material procurement, and presents an agent-based system, named C-Negotiators, that helps a contractor and suppliers to negotiate via the Internet. Genetic algorithm is used to find the most beneficial agreement for all parties, and web-based development is used to improve negotiation efficiency. Experiments also were conducted and demonstrated that C-Negotiators improved negotiation efficiency by saving negotiation time and cost, and improved negotiation effectiveness by suggesting a better agreement with higher joint payoff. Although the increase in payoff was smaller than expected, the improvement should increase for more complex negotiation problems involving more issues and options, or complicated preferences and for inexperienced negotiators. The application of the system is mainly limited by its symmetric optimization, while procurement negotiations in the construction industry are biased towards the contractor, and also by user comfort with their preferences and negotiations being monitored by the system.