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- K.I. Wang [2]
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In this paper, a novel physical testbed for intelligent environments and its software based multi-agent control system are presented. In the physical testbed, a fair amount of embedded sensors and actuators are interconnected in three types of physical networks, namely the LonWorks network, RS-485 network and IP network. Universal plug and play (UPnP) is introduced in the system architecture to provide unique control interface between high level multi-agent control system and low level devices on different physical networks. The multi-agent control system is developed on an existing agent platform, JAVA agent development framework (JADE). Fuzzy inference learning is implemented with multiple fuzzy inference agents, each models the human behaviour associated with a predefined group of devices in forms of fuzzy rules. Corresponding fuzzy logic controller agents can be initiated to provide user preferred control actions according to the fuzzy rule bases. A comparative analysis shows that our control system achieves a great improvement in both control accuracy and computational efficiency compared to other offline control systems. Online adaptive learning, automatic device group formation and advanced wireless device networks are within the scope of our system architecture. (10 pages)
This paper discusses a multi-agent system (MAS) core developed for an intelligent environment (IE) application. MAS poses features like distributed control, cooperative decision making, and remote monitoring which make it suitable for IE applications. The proposed MAS consists of four intelligent components namely accumulative personal preferences learning, intelligent resources allocation, reactive controls and context-aware proactive controls. It directly interacts with the occupants of the IE and interfaces with the underlying physical device control software. The tool used to develop the MAS is called JADE (Java Agent DEvelopment Framework), a FIPA (Foundation of Intelligent Physical Agent)-compliant development framework. It contains a group of libraries to assist the development of MASs, a platform independent runtime environment and a set of graphical tools for monitoring and debugging the designed system. The preliminary MAS core is developed and tested in Windows XP environment and is implemented only with simple reactive controls. At later stage of the research, the reactive controls will be strengthened and other intelligent components will be added to complete the system. The ultimate goal of this project is to achieve a fully integrated proactive control system via context-aware, cooperative multi-agent learning and decision making. (6 pages)
