Title :
An Adaptive Multi-agent Model for Automated Negotiation
Author :
Radu, Serghei ; Lungu, Valentin
Author_Institution :
Dept. of Comput. Sci., Univ. Politeh. of Bucharest, Bucharest, Romania
Abstract :
The paper presents a model of heuristic negotiation between self-interested agents, which allows negotiation over multiple issues and learns the agent´s negotiation strategy. The agents are using different strategies to negotiate and several models to adjust their decision during negotiation. They are capable of increasing their performance with the experience, by adapting to the environment conditions. The agents´ performance, using multiple tactics, is compared to the agents having learning capabilities, based on reinforcement learning techniques. Several tests are performed, in a scenario similar to the TAC-SCM environment.
Keywords :
learning (artificial intelligence); multi-agent systems; TAC-SCM environment; adaptive multi-agent model; agent negotiation strategy; automated agent negotiation; heuristic agent negotiation; reinforcement learning; Adaptation models; Computational modeling; Decision making; Learning (artificial intelligence); Multi-agent systems; Proposals; Protocols; automated negotiation; multi-agent system; reinforcement learning;
Conference_Titel :
Control Systems and Computer Science (CSCS), 2013 19th International Conference on
Conference_Location :
Bucharest
Print_ISBN :
978-1-4673-6140-8
DOI :
10.1109/CSCS.2013.76
