proposed multi-agent control system with hybrid intelligence achieves ..... ten output features including 4 dimmable lights, blinds, desk light, bed light, heater.
Multi-agent System with Hybrid Intelligence Using Neural Network and Fuzzy Inference Techniques Kevin I-Kai Wang, Waleed H. Abdulla, and Zoran Salcic Department of Electrical and Computer Engineering, University of Auckland. Private Bag 92019, Auckland, New Zealand {kevin.wang,w.abdulla,z.salcic}@auckland.ac.nz
Abstract. In this paper, a novel multi-agent control system incorporating hybrid intelligence and its physical testbed are presented. The physical testbed is equipped with a large number of embedded devices interconnected by three types of physical networks. It mimics a ubiquitous intelligent environment and allows real-time data collection and online system evaluation. Human control behaviours for different physical devices are analysed and classified into three categories. Physical devices are grouped based on their relevance and each group is assigned to a particular behaviour category. Each device group is independently modelled by either fuzzy inference or neural network agents according to the behaviour category. Comparative analysis shows that the proposed multi-agent control system with hybrid intelligence achieves significant improvement in control accuracy compared to other offline control systems.
1 Introduction With advanced embedded technology, computational devices ubiquitously merge with individuals’ daily activities. The demand for devices, applications, and processes to become more intelligent has increased dramatically due to insufficient availability of human supervisions and abundance of computational resources. Thus, intelligent systems are developed and are expected to be able to adapt, to predict and to have high level of autonomy [1]. More specifically, intelligent systems should be aware of the environment context, be able to model and adapt to user’s behaviour and respond on user’s behalf [2]. The MIT AI Lab started Intelligent Environment (IE) researches around mid 90s [3]. At that time, their research focus was to introduce intelligence via smart sensors and camera networks and can be considered as Human-Computer Interaction (HCI) and sensor network research. In 1999, a Multi-Agent System (MAS) called Metaglue which had no built-in intelligence, was developed to control the IE [4]. However, in the past few years, intelligent knowledge-based resource management [5] and reactive behaviour systems [6] had been developed and integrated into the MAS to introduce intelligence. The Adaptive Building Intelligence (ABI) project collaborated by several Swiss universities uses MAS approach to provide general building services rather than H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 473–482, 2007. © Springer-Verlag Berlin Heidelberg 2007
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personal space services [7]. The University of Essex focuses on online learning of personalised behaviour using Fuzzy Inference System (FIS) [8]. In University of Colorado, Artificial Neural Networks (ANNs) are used to control the lighting and heating services of a normal residential environment involving different types of living spaces such dining room, living room and bathroom [9]. There are also many other research efforts such as the Microsoft Smart House [10], IBM BlueSpace [11] and MASSIHN project [12]. However, most of them focus on integrating working behaviour and device automation into the control system. These control systems neither capture or model the human behaviour nor adapt to human needs, and do not reveal the true meaning of intelligence. In this paper, user control behaviours occurred on different physical devices are analysed. Based on the analysis, two soft computing techniques, neural network and Multi-Agent Fuzzy Inference System (MAFIS) are used to model different control behaviours. A novel Multi-Agent Neural Net Fuzzy Inference System (MANNFIS) is developed to merge the two techniques into one multi-agent control system. In addition to the high level software control system, a complete system architecture including middleware layer and underlying hardware infrastructure, has been proposed. A physical testbed named Distributed Embedded Intelligence Room (DEIR) has been constructed according to the system architecture to enable real-time system evaluation and data collection. The paper is laid out as follows. Section 2 introduces the physical infrastructure and system architecture of the testbed. Section 3 explains the soft computing techniques used and the rationales of using those techniques. Section 4 compares the performances of the proposed control system with other contemporary control systems. Section 5 gives ongoing research directions for the project and section 6 concludes the paper.
2 Distributed Embedded Intelligence Room (DEIR) Distributed Embedded Intelligence Room (DEIR) is the physical testbed designed by the Embedded Systems Research Group of the University of Auckland [13]. As shown in Fig. 1, DEIR resembles the target environment such as personal office, single studio accommodation, and convalescent nursing room. DEIR is equipped with
Fig. 1. Distributed Embedded Intelligence Room (DEIR)
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a number of embedded sensors including light intensity, temperature, pressure, smoke, and motion sensors to monitor the environment states. It also contains a number of actuators for automating windows, blinds, dimmable lights and house appliances. The underlying physical infrastructure is comprised of three different device networks for connecting various types of sensors and devices. Middleware is incorporated in the software counterpart of the devices, the device agents, to integrate hybrid physical networks and to improve system flexibility on future extensions. High level multi-agent control system communicates with the device agents through middleware channels to exchange context information and to issue control commands.
Fig. 2. DEIR system architecture
2.1 DEIR Physical Infrastructure Refer to Fig. 2, three physical networks namely IP network, LonWorks network [14] and RS-485 network are used to interconnect all the devices implemented in DEIR. Each network has its own protocol, control software and control hardware. In LonWorks network, all devices are connected to the iLon100 gateway server which communicates with the control software, the LNS server. In RS-485 network, devices are separately grouped and controlled by smart hardware switches. Each smart switch has a M16C microcontroller, a infra-red receiver, and a RS-485 network connection. The devices can be controlled through traditional switch interface, infra-red remote control and RS-485 network commands. All the smart switches are connected to another RS-485 gateway server to exchange RS-485 messages with the software device agents. Different to LonWorks and RS-485 networks, IP network is not solely used as a device network, but it is also used by the middleware to integrate other physical networks. The middleware layer allows new device networks to be added into the system architecture easily. Wireless device networks such as Zigbee and Bluetooth are possible extensions for the system.
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2.2 Middleware and Multi-agent Platform In DEIR, Universal Plug and Play (UPnP) is used as the middleware. The implementation of UPnP protocol can be considered in two parts: the control point and device agents. Control point agent is the server component which keeps all the information of registered device agents and acts as a software interface between high level control and device agents. Device agent is the client component which links the control point agent with corresponding network control software such as LNS server or sends out commands directly as in RS-485 network. The UPnP protocol is implemented using CyberGarage UPnP Java API [15]. In order to integrate all the software entities smoothly, a widely used and tested agent platform, JADE (Java Agent DEvelopment Framework), was selected [16]. Agents of JADE are developed using the provided Java API which enables UPnP components to be implemented as JADE agents directly. JADE also provide extensive plug-ins such as J2ME support, which helps in developing mobile agent applications.
3 Hybrid Learning Techniques The ultimate goal of the proposed multi-agent control system is to capture user habits and to provide autonomous monitoring and controlling services in ubiquitous intelligent spaces. In the previous multi-agent control system, Multi-Agent Fuzzy Inference System (MAFIS) was the only technique used to capture and to model user control behaviours [13]. It was found that MAFIS can not satisfactorily model all kinds of control behaviours. To improve control accuracy, another modelling technique, neural network, is introduced in the system to model part of the user behaviours. The control behaviours are analysed to justify the use of MAFIS and neural network and to assign each device with the most suitable modelling technique. The proposed multi-agent control system incorporating hybrid intelligence is referred as the Multi-Agent Neural Net Fuzzy Inference System (MANNFIS). 3.1 Device Behaviour Categorisation In order to select the optimal modelling technique, user control behaviours need to be categorised based on realistic data analysis. Based on the analysis, three behaviour categories are defined in MANNFIS. The first category involves common reactive control behaviours which rarely change among different users. This type of behaviour can easily be modelled using a traditional rule-based system. Security control such as autonomous door lock in absence of occupants is a good example. Due to the fact that this type of behaviour contains minimum uncertainties and the corresponding modelling technique requires no learning, they will not be included in the discussions. The second category involves daily routine behaviours that constantly follow certain environment context(s). This type of behaviour is typically user dependent but contains low uncertainties. In the past experiments, curtain devices show the exact characteristics of this category. Fig. 3 shows the curtain control behaviour collected
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over 3 consecutive days. It can be depicted that the curtain is turned on (i.e. it is closed) when external light intensity drops below five. Despite the fact that different users might control the curtain with different environmental parameters or parameter values, such personal preferences should not vary very often. Thus, a feed-forward
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neural network is a suitable tool to classify (or model) the device status according the relevant environmental parameters. The last category consists of behaviours that follow certain trend roughly but could be highly non-deterministic. Typical device that characterises this type of behaviour is the dimmable light as shown in Fig. 4. There is a rough trend to increase the light level when external light intensity drops such that the ambient light level is maintained. With great tolerance of uncertainties, MAFIS has been proved to handle this type of devices with great accuracy in the previous multi-agent control system [13]. In MANNFIS, devices are grouped with relevant environmental parameters to enhance the quality of model. Each device group is pre-assigned to a particular behaviour category according to the analysis. Based on the category, different modelling techniques are applied to different device groups. Detailed device groupings are shown in Fig. 5. The same groupings are used to execute the performance evaluation, which is discussed in Section 4.
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Fig. 5. Device Grouping in MANNFIS
Fig. 6. MANNFIS architecture
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3.2 Multi-Agent Neural Net Fuzzy Inference System (MANNFIS) As shown in Fig. 6, MANNFIS is a combination of neural network and MAFIS. The neural network implemented in MANNFIS is the most common feed-forward MultiLayer Perceptron (MLP) [17]. Numerical data that represents user control behaviours is used to train the neural network with back propagation algorithm. Detailed implementation can be found in [17]. MAFIS, on the other hand, was the first soft computing technique selected in DEIR project due to its great tolerance of uncertainties. The learning technique used in MAFIS is adopted from the Adaptive Online Fuzzy Inference System (AOFIS) developed at the University of Essex [8]. The numerical data are first processed by double clustering algorithm which generates fuzzy granules [18]. Fuzzy granules are then quantified using Gaussian membership functions. Eventually, fuzzy rules are extracted based on the input data and Gaussian membership functions, using the extended Mendel Wang’s method [19]. The implementation of MAFIS was provided in our previous publication [15].
4 Performance Analysis To examine the control accuracy of MANNFIS, a comparative analysis with other offline control systems has been performed. In order to conduct a fair comparison, the dataset provided by our colleagues at the University of Essex is used to evaluate all the control systems. The dataset used for the analysis contains seven input features, namely internal light sensor, external light sensor, internal temperature sensor, external temperature sensor, chair pressure sensor, bed pressure sensor and time; and ten output features including 4 dimmable lights, blinds, desk light, bed light, heater and two PC applications: MS Word and MS Media Player. This particular dataset contains 408 data instances collected over three consecutive days monitoring real user activities. The data instances are split into 272 data instances in the training set and 136 data instances in the testing set. Before evaluating the system performance, the main difference between MANNFIS and other control systems should be addressed. MANNFIS is a multi-agent based system rather than a centralised system as the others. Refer to Fig. 5, output devices are grouped with their relevant input devices where each device group can be modelled and controlled independently in parallel. The Scaled Root Mean Square Error (SRMSE) is used to measure the control accuracy. The traditional RMSE is scaled by the dynamic range of the output to take into consideration the different dynamic ranges of output devices. As shown in Fig. 7, both MLP and MAFIS perform well in modelling their assigned device groups. On the other hand, the computational efficiency (i.e. the execution time) for MLP is much higher compare to MAFIS due to two reasons. First, MAFIS is used to model control behaviours with high uncertainties, and usually works on a multi-dimensional feature space. Second, MAFIS requires fair amount of fuzzy sets to achieve good control accuracy. Nevertheless, by taking advantages of the multi-agent architecture, device groups can be further divided to make MAFIS more suitable for embedded applications. Fig. 8 shows the performance comparison with other control systems including AOFIS, Genetic Programming (GP), Adaptive Network-based Fuzzy Inference System (ANFIS), MLP and MAFIS. By introducing neural network into the
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control system, MANNFIS achieves up to 94% control accuracy, which is 15% improvements in control accuracy compared to its predecessor, MAFIS. It is also clear that MANNFIS outperforms other control systems by generating around 50% less control errors. Group 1
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5 Future Works So far, the algorithms implemented in and the evaluation performed on MANNFIS and its predecessor MAFIS are all offline. The next step is to incorporate online adaptation ability in the algorithms and perform real-time data collection and system performance evaluation. With sufficient real-time data, a more thorough user control behaviour analysis could be carried out. Eventually, an algorithm should be developed to autonomously classify behaviours into different categories and to assign the most suitable modelling and controlling techniques.
6 Conclusions In this paper, a physical intelligent environment testbed, DEIR, and a multi-agent control system with hybrid intelligence, MANNFIS, are presented. DEIR consists of a large number of embedded devices, which are interconnected by hybrid physical networks. Extra layer of middleware is introduced in the system architecture to integrate hybrid physical networks and to improve system flexibility for future extension. DEIR resembles a true ubiquitous intelligent environment which allows real-time data collection and online system evaluation. MANNFIS is the top level control in DEIR system architecture, implemented using JADE agent platform. It employs two types of learning techniques, namely MAFIS and MLP, to model different types of human control behaviours. Devices to be controlled are grouped in terms of their relevance and modelled by either MAFIS or MLP at the same time. Performance analysis shows that both MAFIS and MLP achieve excellent accuracy in controlling the assigned device groups. The analysis also shows that MANNFIS outperforms other offline control systems by generating about 50% less control errors.
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Acknowledgement. This research is supported by UARC research grant 3604552 and top achiever doctoral scholarship. The authors would like to thank Dr. Faiyaz Doctor, Prof. Victor Callaghan and Prof. Hani Hagras for their kind contribution of providing the dataset for comparative analysis and various helps regarding to the use of their AOFIS learning technique.
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