Thesis maps the situation in the telecommunications market in the Czech Republic, analyzes the major players on the Czech nad world market, and analyzes the possibilities of marketing and presentation of high-tech to customers in user-friendly form.

L'objectif de ce travail de thèse est de proposer un canevas sémantique intégrant un méta-modèle et des outils de raisonnement permettant à tout concepteur de système ubiquitaire de mettre en oeuvre facilement des mécanismes de gestion des politiques de la vie privée. Le canevas proposé intègre une architecture middleware générique qui offre des composants pour définir, administrer et contrôler l'application des politiques de confidentialité. Notre approche proposée est hybride. Elle est fondée sur l’ingénierie dirigée par les modèles et sur un raisonnement à base d'ontologies et de règles d'inférence opérant selon l'hypothèse du monde clos. Le méta-modèle proposé est caractérisé par un niveau d'abstraction et d'expressivité élevé permettant de définir des politiques de gestion de la vie privée indépendamment du domaine d'application pouvant être adaptées à différents contextes. Il définit, aussi, un cadre conceptuel pour établir des modèles de règles génériques et décidables permettant de prendre des décisions de contrôle cohérentes pour la protection de la vie privée. Ces modèles de règles sont mis en oeuvre grâce au langage de règles SmartRules permettant de mettre en oeuvre un contrôle adaptatif. Ce dernier est basé sur un raisonnement non-monotone et une représentation des instances de concepts selon la supposition du nom unique. Nous avons validé le canevas proposé à travers un scénario typique mettant en oeuvre des services d'assistance ambiante sensibles à la vie privée de personne âgée. / This thesis aims at proposing a semantic framework that integrates a meta-model and reasoning tools allowing any ubiquitous system designer to easily implement mechanisms to manage privacy policies. The proposed framework includes a generic middleware architecture that provides components to define, manage and monitor the implementation of privacy policies. Our approach is an hybrid one based on Model-Driven Engineering and a reasoning based on ontologies and inference rules operating on the assumption of the closed world. The proposed meta-model is characterized by a high level of abstraction and expressiveness to define privacy policies management regardless of the domain application and can be adapted to different contexts. It defines, also, a conceptual framework for generic decidable modelling rules to make consistent control decisions on user privacy. These model rules are implemented using the SmartRules language that could implement an adaptive control. The latter is based on a non-monotonic reasoning and representation of instances of concepts according to the unique name assumption. We have validated the proposed semantic framework through a typical scenario that implements support ambient intelligence privacy-aware services for elderly.

The vision of Ambient Intelligence (AmI) can be realised through the integration of embedded technologies, distributed systems, middleware and human machine interfaces and many research efforts have been made to advance these technologies. However, the exclusiveness of these ambient intelligence technologies has reduced their practical values. In this thesis, a novel AmI platform is proposed to facilitate the integration and interoperability of various technologies in the process of developing AmI applications. The platform defines the overall software/hardware architecture and communication interfaces and provides a common base for development, operation and future adaptation of AmI applications. The proposed platform consists of four layers, the physical ubiquitous environment, middleware, multi-agent system (MAS) and application layer. The ubiquitous environment layer accommodates any type of embedded device network for interconnecting different sensors, actuators and computing devices. The middleware layer is built using an IP-based service discovery protocol, Universal Plug and Play (UPnP), which provides a unique communication interface for controlling and monitoring embedded devices. The MAS handles the core distributed and adaptive control functionality and communication with user interfaces. The application layer contains any type of user interface for different AmI applications. An XML-based content language is designed with an XML schema and seven XML messages. The content language standardises the way of interpreting contents of communication between different user interfaces and the MAS. Based on the proposed platform, a complete AmI application prototype called Distributed Embedded Intelligence Room (DEIR) has been implemented. Four different device networks, the SmartHouse network, IP network, Bluetooth and Zigbee network, have been integrated in DEIR to interconnect various embedded sensors and devices. The MAS is implemented using Java Agent DEvelopment framework (JADE). Four application specific agents, known as the UPnP control point agent, IP interface agent, fuzzy inference agent and decision tree agent, are designed and implemented. The UPnP control point agent provides MAS the ability to monitor and to control the underlying hardware devices through the UPnP middleware layer. The IP interface agent handles communication with user interfaces over socket connections. Fuzzy inference and decision tree agents are implemented to provide personalised learning and automated control capabilities. Three user interfaces, including a remote graphical user interface, a mobile PDA interface and a 3D virtual reality interface are implemented. Contents of communication between these user interfaces and the MAS are encoded using the proposed XML content language and transmitted over socket connections. The AmI application prototype, DEIR, has demonstrated the ability of integrating multiple device networks and multiple user interfaces, which is a vital feature for most AmI applications. Two case studies have been carried out to incorporate two adaptive learning and controlling algorithms, known as the adaptive online fuzzy inference system (AOFIS) and ID3 decision tree algorithm, in the MAS of DEIR. The results of case studies show that DEIR has the ability of incorporating multiple adaptive control algorithms as multiple agents. In addition, comparable or better offline learning accuracy and learning speed have been achieved by DEIR compared with other advanced adaptive control algorithms. / Whole document restricted, but available by request, use the feedback form to request access.

<p>This work focuses on the socio-technical aspects of artificial intelligence, namely how (specific types of) intelligent systems function in human workplace environments. The goal is first to get a better understanding of human needs and expectations when it comes to interaction with intelligent systems, and then to make use of the understanding gained in the process of designing and implementing such systems.</p><p>The work presented focusses on a specific problem in developing intelligent systems, namely how the artefacts to be developed can fit smoothly into existing socio-cultural settings. To achieve this, we make use of theories from the fields of organisational psychology, sociology, and linguistics. This is in line with approaches commonly found in AI. However, most of the existing work deals with individual aspects, like how to mimic the behaviour or emulate methods of reasoning found in humans, whereas our work centers around the social aspect. Therefore, we base our work on theories that have not yet gained much attention in intelligent systems design. To be able to make them fruitful for intelligent systems research and development, we have to adapt them to the specific settings, and we have to transform them to suit the practical problems at hand.</p><p>The specific theoretical frameworks we draw on are first and foremost activity theory and to a lesser degree semiotics. Activity theory builds on the works of Leont'ev. It is a descriptive tool to help understand the unity of consciousness and activity. Its focus lies on individual and collective work practise. One of its strengths, and the primary reason for its value in AI development, is the ability to identify the role of material artefacts in the work process. Halliday's systemic functional theory of language (SFL) is a social semiotic theory that sets out from the assumption that humans are social beings that are inclined to interact and that this interaction is inherently multimodal. We interact not just with each other, but with our own constructions and with our natural world. These are all different forms of interaction, but they are all sign processes.</p><p>Due to the obvious time and spatial constraints, we cannot address all of the challenges that we face when building intelligent artefacts. In reducing the scope of the thesis, we have focused on the problem of explanation, and here in particular the problem of explanation from a user perspective. By putting social theories to work in the field of artificial intelligence, we show that results from other fields can be beneficial in understanding what explanatory capabilities are needed for a given intelligent system, and to ascertain in which situations an explanation should be delivered. Besides lessons learned in knowledge based system development, the most important input comes from activity theory.</p><p>The second focus is the challenge of contextualisation. Here we show that work in other scientific fields can be put to use in the development of context aware or ambient intelligent systems. Again, we draw on results from activity theory and combine this with insights from semiotics.</p><p>Explanations are themselves contextual, so the third challenge is to explore the space spanned by the two dimensions ability to explain and contextualisation. Again, activity theory is beneficial in resolving this issue.</p><p>The different theoretical considerations have also led to some practical approaches. Working with activity theory helps to better understand what the relevant contextual aspects of a given application are and helps to develop models of context which are both grounded in the tradition of context aware systems design and are plausible from a cognitive point of view.</p><p>Insights from an analysis of research in the knowledge based system area and activity theory have further lead to the amendment of a toolbox for requirements engineering, so called problem frames. New problem frames that target explanation aware ambient intelligent systems are presented. This is supplemented with work looking at the design of an actual system after the requirements have been elicited and specified. Thus, the socio-technical perspective on explanations is coupled with work that addresses knowledge representation issues, namely how to model sufficient knowledge to be able to deliver explanations.</p>

The vision of Ambient Intelligence (AmI) can be realised through the integration of embedded technologies, distributed systems, middleware and human machine interfaces and many research efforts have been made to advance these technologies. However, the exclusiveness of these ambient intelligence technologies has reduced their practical values. In this thesis, a novel AmI platform is proposed to facilitate the integration and interoperability of various technologies in the process of developing AmI applications. The platform defines the overall software/hardware architecture and communication interfaces and provides a common base for development, operation and future adaptation of AmI applications. The proposed platform consists of four layers, the physical ubiquitous environment, middleware, multi-agent system (MAS) and application layer. The ubiquitous environment layer accommodates any type of embedded device network for interconnecting different sensors, actuators and computing devices. The middleware layer is built using an IP-based service discovery protocol, Universal Plug and Play (UPnP), which provides a unique communication interface for controlling and monitoring embedded devices. The MAS handles the core distributed and adaptive control functionality and communication with user interfaces. The application layer contains any type of user interface for different AmI applications. An XML-based content language is designed with an XML schema and seven XML messages. The content language standardises the way of interpreting contents of communication between different user interfaces and the MAS. Based on the proposed platform, a complete AmI application prototype called Distributed Embedded Intelligence Room (DEIR) has been implemented. Four different device networks, the SmartHouse network, IP network, Bluetooth and Zigbee network, have been integrated in DEIR to interconnect various embedded sensors and devices. The MAS is implemented using Java Agent DEvelopment framework (JADE). Four application specific agents, known as the UPnP control point agent, IP interface agent, fuzzy inference agent and decision tree agent, are designed and implemented. The UPnP control point agent provides MAS the ability to monitor and to control the underlying hardware devices through the UPnP middleware layer. The IP interface agent handles communication with user interfaces over socket connections. Fuzzy inference and decision tree agents are implemented to provide personalised learning and automated control capabilities. Three user interfaces, including a remote graphical user interface, a mobile PDA interface and a 3D virtual reality interface are implemented. Contents of communication between these user interfaces and the MAS are encoded using the proposed XML content language and transmitted over socket connections. The AmI application prototype, DEIR, has demonstrated the ability of integrating multiple device networks and multiple user interfaces, which is a vital feature for most AmI applications. Two case studies have been carried out to incorporate two adaptive learning and controlling algorithms, known as the adaptive online fuzzy inference system (AOFIS) and ID3 decision tree algorithm, in the MAS of DEIR. The results of case studies show that DEIR has the ability of incorporating multiple adaptive control algorithms as multiple agents. In addition, comparable or better offline learning accuracy and learning speed have been achieved by DEIR compared with other advanced adaptive control algorithms. / Whole document restricted, but available by request, use the feedback form to request access.

This work focuses on the socio-technical aspects of artificial intelligence, namely how (specific types of) intelligent systems function in human workplace environments. The goal is first to get a better understanding of human needs and expectations when it comes to interaction with intelligent systems, and then to make use of the understanding gained in the process of designing and implementing such systems. The work presented focusses on a specific problem in developing intelligent systems, namely how the artefacts to be developed can fit smoothly into existing socio-cultural settings. To achieve this, we make use of theories from the fields of organisational psychology, sociology, and linguistics. This is in line with approaches commonly found in AI. However, most of the existing work deals with individual aspects, like how to mimic the behaviour or emulate methods of reasoning found in humans, whereas our work centers around the social aspect. Therefore, we base our work on theories that have not yet gained much attention in intelligent systems design. To be able to make them fruitful for intelligent systems research and development, we have to adapt them to the specific settings, and we have to transform them to suit the practical problems at hand. The specific theoretical frameworks we draw on are first and foremost activity theory and to a lesser degree semiotics. Activity theory builds on the works of Leont'ev. It is a descriptive tool to help understand the unity of consciousness and activity. Its focus lies on individual and collective work practise. One of its strengths, and the primary reason for its value in AI development, is the ability to identify the role of material artefacts in the work process. Halliday's systemic functional theory of language (SFL) is a social semiotic theory that sets out from the assumption that humans are social beings that are inclined to interact and that this interaction is inherently multimodal. We interact not just with each other, but with our own constructions and with our natural world. These are all different forms of interaction, but they are all sign processes. Due to the obvious time and spatial constraints, we cannot address all of the challenges that we face when building intelligent artefacts. In reducing the scope of the thesis, we have focused on the problem of explanation, and here in particular the problem of explanation from a user perspective. By putting social theories to work in the field of artificial intelligence, we show that results from other fields can be beneficial in understanding what explanatory capabilities are needed for a given intelligent system, and to ascertain in which situations an explanation should be delivered. Besides lessons learned in knowledge based system development, the most important input comes from activity theory. The second focus is the challenge of contextualisation. Here we show that work in other scientific fields can be put to use in the development of context aware or ambient intelligent systems. Again, we draw on results from activity theory and combine this with insights from semiotics. Explanations are themselves contextual, so the third challenge is to explore the space spanned by the two dimensions ability to explain and contextualisation. Again, activity theory is beneficial in resolving this issue. The different theoretical considerations have also led to some practical approaches. Working with activity theory helps to better understand what the relevant contextual aspects of a given application are and helps to develop models of context which are both grounded in the tradition of context aware systems design and are plausible from a cognitive point of view. Insights from an analysis of research in the knowledge based system area and activity theory have further lead to the amendment of a toolbox for requirements engineering, so called problem frames. New problem frames that target explanation aware ambient intelligent systems are presented. This is supplemented with work looking at the design of an actual system after the requirements have been elicited and specified. Thus, the socio-technical perspective on explanations is coupled with work that addresses knowledge representation issues, namely how to model sufficient knowledge to be able to deliver explanations.

The vision of Ambient Intelligence (AmI) can be realised through the integration of embedded technologies, distributed systems, middleware and human machine interfaces and many research efforts have been made to advance these technologies. However, the exclusiveness of these ambient intelligence technologies has reduced their practical values. In this thesis, a novel AmI platform is proposed to facilitate the integration and interoperability of various technologies in the process of developing AmI applications. The platform defines the overall software/hardware architecture and communication interfaces and provides a common base for development, operation and future adaptation of AmI applications. The proposed platform consists of four layers, the physical ubiquitous environment, middleware, multi-agent system (MAS) and application layer. The ubiquitous environment layer accommodates any type of embedded device network for interconnecting different sensors, actuators and computing devices. The middleware layer is built using an IP-based service discovery protocol, Universal Plug and Play (UPnP), which provides a unique communication interface for controlling and monitoring embedded devices. The MAS handles the core distributed and adaptive control functionality and communication with user interfaces. The application layer contains any type of user interface for different AmI applications. An XML-based content language is designed with an XML schema and seven XML messages. The content language standardises the way of interpreting contents of communication between different user interfaces and the MAS. Based on the proposed platform, a complete AmI application prototype called Distributed Embedded Intelligence Room (DEIR) has been implemented. Four different device networks, the SmartHouse network, IP network, Bluetooth and Zigbee network, have been integrated in DEIR to interconnect various embedded sensors and devices. The MAS is implemented using Java Agent DEvelopment framework (JADE). Four application specific agents, known as the UPnP control point agent, IP interface agent, fuzzy inference agent and decision tree agent, are designed and implemented. The UPnP control point agent provides MAS the ability to monitor and to control the underlying hardware devices through the UPnP middleware layer. The IP interface agent handles communication with user interfaces over socket connections. Fuzzy inference and decision tree agents are implemented to provide personalised learning and automated control capabilities. Three user interfaces, including a remote graphical user interface, a mobile PDA interface and a 3D virtual reality interface are implemented. Contents of communication between these user interfaces and the MAS are encoded using the proposed XML content language and transmitted over socket connections. The AmI application prototype, DEIR, has demonstrated the ability of integrating multiple device networks and multiple user interfaces, which is a vital feature for most AmI applications. Two case studies have been carried out to incorporate two adaptive learning and controlling algorithms, known as the adaptive online fuzzy inference system (AOFIS) and ID3 decision tree algorithm, in the MAS of DEIR. The results of case studies show that DEIR has the ability of incorporating multiple adaptive control algorithms as multiple agents. In addition, comparable or better offline learning accuracy and learning speed have been achieved by DEIR compared with other advanced adaptive control algorithms. / Whole document restricted, but available by request, use the feedback form to request access.

The vision of Ambient Intelligence (AmI) can be realised through the integration of embedded technologies, distributed systems, middleware and human machine interfaces and many research efforts have been made to advance these technologies. However, the exclusiveness of these ambient intelligence technologies has reduced their practical values. In this thesis, a novel AmI platform is proposed to facilitate the integration and interoperability of various technologies in the process of developing AmI applications. The platform defines the overall software/hardware architecture and communication interfaces and provides a common base for development, operation and future adaptation of AmI applications. The proposed platform consists of four layers, the physical ubiquitous environment, middleware, multi-agent system (MAS) and application layer. The ubiquitous environment layer accommodates any type of embedded device network for interconnecting different sensors, actuators and computing devices. The middleware layer is built using an IP-based service discovery protocol, Universal Plug and Play (UPnP), which provides a unique communication interface for controlling and monitoring embedded devices. The MAS handles the core distributed and adaptive control functionality and communication with user interfaces. The application layer contains any type of user interface for different AmI applications. An XML-based content language is designed with an XML schema and seven XML messages. The content language standardises the way of interpreting contents of communication between different user interfaces and the MAS. Based on the proposed platform, a complete AmI application prototype called Distributed Embedded Intelligence Room (DEIR) has been implemented. Four different device networks, the SmartHouse network, IP network, Bluetooth and Zigbee network, have been integrated in DEIR to interconnect various embedded sensors and devices. The MAS is implemented using Java Agent DEvelopment framework (JADE). Four application specific agents, known as the UPnP control point agent, IP interface agent, fuzzy inference agent and decision tree agent, are designed and implemented. The UPnP control point agent provides MAS the ability to monitor and to control the underlying hardware devices through the UPnP middleware layer. The IP interface agent handles communication with user interfaces over socket connections. Fuzzy inference and decision tree agents are implemented to provide personalised learning and automated control capabilities. Three user interfaces, including a remote graphical user interface, a mobile PDA interface and a 3D virtual reality interface are implemented. Contents of communication between these user interfaces and the MAS are encoded using the proposed XML content language and transmitted over socket connections. The AmI application prototype, DEIR, has demonstrated the ability of integrating multiple device networks and multiple user interfaces, which is a vital feature for most AmI applications. Two case studies have been carried out to incorporate two adaptive learning and controlling algorithms, known as the adaptive online fuzzy inference system (AOFIS) and ID3 decision tree algorithm, in the MAS of DEIR. The results of case studies show that DEIR has the ability of incorporating multiple adaptive control algorithms as multiple agents. In addition, comparable or better offline learning accuracy and learning speed have been achieved by DEIR compared with other advanced adaptive control algorithms. / Whole document restricted, but available by request, use the feedback form to request access.

The vision of Ambient Intelligence (AmI) can be realised through the integration of embedded technologies, distributed systems, middleware and human machine interfaces and many research efforts have been made to advance these technologies. However, the exclusiveness of these ambient intelligence technologies has reduced their practical values. In this thesis, a novel AmI platform is proposed to facilitate the integration and interoperability of various technologies in the process of developing AmI applications. The platform defines the overall software/hardware architecture and communication interfaces and provides a common base for development, operation and future adaptation of AmI applications. The proposed platform consists of four layers, the physical ubiquitous environment, middleware, multi-agent system (MAS) and application layer. The ubiquitous environment layer accommodates any type of embedded device network for interconnecting different sensors, actuators and computing devices. The middleware layer is built using an IP-based service discovery protocol, Universal Plug and Play (UPnP), which provides a unique communication interface for controlling and monitoring embedded devices. The MAS handles the core distributed and adaptive control functionality and communication with user interfaces. The application layer contains any type of user interface for different AmI applications. An XML-based content language is designed with an XML schema and seven XML messages. The content language standardises the way of interpreting contents of communication between different user interfaces and the MAS. Based on the proposed platform, a complete AmI application prototype called Distributed Embedded Intelligence Room (DEIR) has been implemented. Four different device networks, the SmartHouse network, IP network, Bluetooth and Zigbee network, have been integrated in DEIR to interconnect various embedded sensors and devices. The MAS is implemented using Java Agent DEvelopment framework (JADE). Four application specific agents, known as the UPnP control point agent, IP interface agent, fuzzy inference agent and decision tree agent, are designed and implemented. The UPnP control point agent provides MAS the ability to monitor and to control the underlying hardware devices through the UPnP middleware layer. The IP interface agent handles communication with user interfaces over socket connections. Fuzzy inference and decision tree agents are implemented to provide personalised learning and automated control capabilities. Three user interfaces, including a remote graphical user interface, a mobile PDA interface and a 3D virtual reality interface are implemented. Contents of communication between these user interfaces and the MAS are encoded using the proposed XML content language and transmitted over socket connections. The AmI application prototype, DEIR, has demonstrated the ability of integrating multiple device networks and multiple user interfaces, which is a vital feature for most AmI applications. Two case studies have been carried out to incorporate two adaptive learning and controlling algorithms, known as the adaptive online fuzzy inference system (AOFIS) and ID3 decision tree algorithm, in the MAS of DEIR. The results of case studies show that DEIR has the ability of incorporating multiple adaptive control algorithms as multiple agents. In addition, comparable or better offline learning accuracy and learning speed have been achieved by DEIR compared with other advanced adaptive control algorithms. / Whole document restricted, but available by request, use the feedback form to request access.

Zugl.: Berlin, Techn. Univ., Diss., 2006 u.d.T.: Herrmann, Klaus: Self-organizing infrastructures for ambient services / Hergestellt on demand