Authors:
Hao Li
;
Ting Pang
;
Yuying Wu
and
Guorui Jiang
Affiliation:
Beijing University of Technology, China
Keyword(s):
Production-Marketing Collaborative Conflict, Multi-Agent, Self-adaptation Negotiation, RBF Neural Network, Q-reinforcement Learning.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Negotiation and Interaction Protocols
;
Software Engineering
;
Symbolic Systems
Abstract:
In order to overcome the lack of adaptability and learning ability of traditional negotiation, we regard supply chain production-marketing collaborative planning negotiation as the research object, design one five-elements negotiation model, adopt a negotiation strategy based on Q-reinforcement learning, and optimize the negotiation strategy by the RBF neural network and predict the information of opponent for adjusting the concession extent. At last, we give a sample that verifies the negotiation strategy can enhance the ability of the negotiation Agents, reduce the negotiation times, and improve the efficiency of resolving the conflicts of production-marketing collaborative planning, comparing to the un-optimized Q-reinforcement learning.