Learning of words in negotiation between autonomous agents

One of the important behaviors of agents in agent-based simulations is negotiation. We focus on the price negotiations where both players have opposing interests as one of the negotiations. In the conventional price negotiation model, the process consists of all alternate succession of offers and counter-offers, exchanging the desired prices directly. As an extended price negotiation model, we propose the price negotiation model whose process consists of an alternate succession of offers of desired price and counter-offers of a virtual word. We propose the agents with a unified learning system who can acquire the ability to distinguish words and use them to negotiate. As a result, we show that the proposed agents can reach an agreement in negotiations and they acquire the negotiation strategy by an evolutionary process