Knowledge sharing approaches for distributed agents system

Considering situations in a multi-agent system, if there are tremendous number of agents sharing knowledge with each other, it is complicated activities hard to be solved. This thesis proposed a method that all agents just connect with a server to alleviate the complexity of the experiences exchange activities. The server collects learning knowledge loaded from all the agents, merges the knowledge, and shares the knowledge to all agents which lack akin experiences. The agents utilized the proposed Pheromone Mechanism in Ant Colony Algorithm to evaluate whether an experience is worthy to upload to the server. Meanwhile, to deal with the problem of massive data processing, this thesis used the open source software, Apache Hadoop, along with the MapReduce programming model. The agents can take shared experiences integrated with their own knowledge to achieve knowledge sharing and increase the efficiency significantly. The proposed approach in this thesis was implemented by a homemade server and personal computers.