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摘要
This article presents a novel intelligent embedded agent approach for reducing the number of associations and interconnections between various agents operating within ad hoc multiagent societies of an Ambient Intelligent Environment (AIE) in order to reduce the processing latency and overheads. The main goal of the proposed fuzzy-based intelligent embedded agents (F-IAS) includes learning the overall network configuration and adapting to the system functionality to personalize themselves to the user needs based on monitoring the user in a lifelong nonintrusive mode. In addition, the F-IAS agents aim to reduce the agent interconnections to the most relevant set of agents in order to reduce the processing overheads and thus implicitly improving the system overall efficiency. We employ embedded ambassador agents, namely embassadors, which are designated F-IAS agents utilized with additional novel characteristics to not only act as a gateway filtering the number of messages multicast across societies but also discover, recommend, and establish associations between agents residing in separate societies. In order to validate the efficiency of the proposed methods for multiagent and society-based intelligent association discovery and learning of F-IAS agents/embassadors we will present two sets of unique experiments. The first experiment describes the obtained results carried out within the intelligent Dormitory (iDorm) which is a real-world testbed for AIE research. Here we specifically demonstrate the utilization of the F-IAS agents and discuss that by optimizing the set of associations the agents increase efficiency and performance. The second set of experiments is based on emulating an iDorm-like large-scale multi-society-based AIE environment. The results illustrate how embassadors discover strongly correlated agent pairs and cause them to form associations so that relevant agents of separate societies can start interacting with each other.
- 出版日期2010-5
