Learning of Virtual Words Utilized in Negotiation Process between Agents

Agent-based simulations are expected to enable analysis of complex social phenomena. In such simulations, one of the important behaviors of the agents is negotiation. Throughout the negotiations, the agents can make complex interactions with each other. Therefore, the ability of agents to perform negotiation is important in simulations of artificial societies. In this paper, we focus on price negotiations, in which the two sides have opposing interests. In the conventional price negotiation model, the process consists of an alternate succession of directly presented offers and counter-offers exchanging the desired prices. As an extended price negotiation model, we introduce virtual words to mimic the negotiation techniques of humans for indirectly presenting the desired price. The process of the proposed negotiation model consists of an alternate succession of offers of desired price and counter-offers of a word. The words represent the degree of the agent’s demand. We propose agents with reinforcement learning who can acquire the ability to distinguish words and use them to negotiate. As a result, we will show that the virtual words became meaningful in the process of negotiations between agents whose negotiating strategies are acquired by reinforcement leaning. key words: price negotiation, reinforcement learning, agent-