Multi-issue negotiation under time constraints
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper presents a method for acquiring a concession strategy of an agent in multi-issue negotiation. This method learns how to make a concession to an opponent for realizing win-win negotiation. To learn the concession strategy, we adopt reinforcement learning. First, an agent receives a proposal from an opponent. The agent recognizes a negotiation state using the difference between their proposals and the difference between their concessions. According to the state, the agent makes a proposal by reinforcement learning. A reward of the learning is a profit of an agreement and a punishment of negotiation breakdown. The experimental results showed that the agents could acquire the negotiation strategy that avoids negotiation breakdown and increases profits of an agreement. As a result, agents can acquire the action policy that strikes a balance between cooperation and competition.