Technical Note: \cal Q-Learning
Machine Learning
Bargaining theory with applications
Bargaining theory with applications
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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This paper focuses on developing human negotiation skills through interactions between a human player and a computer agent, and explores its strategic method towards a human skill improvement in enterprise. For this purpose, we investigate the negotiation skill development through bargaining game played by the player and an agent. Since the acquired negotiation strategy of the players is affected by the negotiation order of the different types of agents, this paper aims at investigating what kind of the negotiation strategies can be learned by negotiating with different kinds of agents in order. Through an intensive human subject experiment, the following implications have been revealed: (1) human players, negotiating with the human-like behavior agent firstly and the strong/weak attitude agent secondly, can neither obtain the large payoff nor win many games, while (2) human players, negotiating with the strong/weak attitude agent firstly and the human-like behavior agent secondly, can obtain the large payoff and win many games.