A Multi-agent System for Electronic Commerce including Adaptive Strategic Behaviours

This work is primarily based on the use of software agents for automated negotiation. We present in this paper a test-bed for agents in an electronic marketplace, through which we simulated different scenarios allowing us to evaluate different agents' negotiation behaviours. The system follows a multi-party and multi-issue negotiation approach. We tested the system by comparing the performance of agents that use multiple tactics with ones that include learning capabilities based on a specific kind of Reinforcement Learning technique. First experiments showed that the adaptive agents tend to win deals over their competitors as their experience increases.