Abstract
11. RELATED WORK Abstract-With the rapid development of multi-agent systems (MAS), on-line automatic negotiation is often needed. But because of incomplete information agents have, the efficiency of on-line negotiation is rather low. To overcome the problem, online reinforcement learning algorithm is presented to learning the incomplete information of negotiation agent to enhance the efficiency of negotiation. The algorithm is applied to on-line bilateral multi-issue negotiation in Multi-agent based electronic commerce. Three kinds of agents are used to compare with, which are no-learning agents (NA), static learning agents (SA) and dynamic learning agent (DA) in this paper. In static learning agent, the learning rate of Q-learning is set to O.lunchangeable, so it’s called static learning. While in dynamic learning proposed by this paper, the learning rate of Q-learning can change dynamically, so it’s called dynamic learning. Experiments show that it can help agents to negotiate more efficiently.
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