A comparison between a communication-based and a data mining-based learning approach for agents 1

Abstract

In complex application systems, there are typically not only autonomous components which can be represented by agents, but humans may also play a role. The interaction between agents and humans can be learned to enhance the stability of a system. How can agents adopt strategies of humans to solve conflict situations? In this paper, we present a learning algorithm for agents based on communication with humans in conflict situations. The learning algorithm consists of four phases: 1) agents detect a conflict situation, 2) a conversation takes place between a human and agents, 3) agents involved in a conflict situation evaluate the strategy applied by the human, and 4) agents that have interacted with humans apply the best rated strategy in a similar conflict situation. We have evaluated this learning algorithm using a Jade/Repast simulation framework. The evaluation study shows that applying the communication-based approach agents adopted the problem solving strategy which has been applied most frequently by humans. We also developed a data mining-based approach which predicts the behavior patterns of humans while deciding a strategy for solving conflicts. A pilot study demonstrates that the data mining-based approach is less effective than the communication based learning approach.