Raymond S.T. Lee – författare

“Anything happens must have its own reason ”. Although I cannot really recall exactly when I heard of this statement for the first time, it is always in my mind and in fact it has been one of the motivations for me to carry out research and study. When I asked myself again about the purpose of writing this book at the time of writing this preface, several “add on” rea sons that had never occurred to me at the start of writing this book in the spring of 2003 surprisingly came up. Back then, when I was preparing the progress report for the iJADE (2. 0) project, a “fuzzy” idea of whether it was feasible to write a book on intelli gent agents came to my mind. This book not only would discuss and deal with the theory but also the “spin off” applications from the iJADE pro ject, including: the iJADE WeatherMan, the iJADE Stock Advisor, the iJADE Surveillant and the latest works on iJADE Negotiator. The fact that I had to launch the iJADE development kit officially over the Web in the summer of 2003 (http://www. ijadk. org) and to arrange courses and semi nars to teach and train our undergraduate students to make use of this tool kit further supported the idea and the future use of this book. Hence, the “archetype” of this book emerged.

“Anything happens must have its own reason ”. Although I cannot really recall exactly when I heard of this statement for the first time, it is always in my mind and in fact it has been one of the motivations for me to carry out research and study. When I asked myself again about the purpose of writing this book at the time of writing this preface, several “add on” rea sons that had never occurred to me at the start of writing this book in the spring of 2003 surprisingly came up. Back then, when I was preparing the progress report for the iJADE (2. 0) project, a “fuzzy” idea of whether it was feasible to write a book on intelli gent agents came to my mind. This book not only would discuss and deal with the theory but also the “spin off” applications from the iJADE pro ject, including: the iJADE WeatherMan, the iJADE Stock Advisor, the iJADE Surveillant and the latest works on iJADE Negotiator. The fact that I had to launch the iJADE development kit officially over the Web in the summer of 2003 (http://www. ijadk. org) and to arrange courses and semi nars to teach and train our undergraduate students to make use of this tool kit further supported the idea and the future use of this book. Hence, the “archetype” of this book emerged.

The scope of this volume is to give to the reader a wide scenario of recent works characterized by a synergistic combination of Soft Computing area with recent trends of Distributed Artificial Intelligence and Ambient Int- ligence. We edited this book for two basic motivations: the emergence of computational intelligence as a mature and integrated science, and the power of the agent paradigm in realizing complex and distributed environments. Computational Intelligence has nowadays a mature formal theory and robust implementation experiences. Intelligent agents have shown their inner ability in acquiring data and knowledge from and performing appropriate de- sions through a functional relation that maps percepts to actions. Compu- tional Intelligence inside Agents are extremely helpful to support the designer of agent-based systems in treating with unknown or ill-defined envir- ments so to better handle knowledge representation and reasoning under these assumptions. The formulation of cooperative strategies, the linguistic aspects of the communication, the local decision versus the global kno- edge, the actions and plans as distributed viewpoints could be characterized by an incomplete knowledge and by a fragmentation of logical inter- tions, faced often by a non-monotonic reasoning. While building “smart” environments many challenges arises. The system needs to interact with its users in order to obtain feedback in a non-intrusive way. Explicit/implicit feedback is needed to take decisions in near-real time.

The scope of this volume is to give to the reader a wide scenario of recent works characterized by a synergistic combination of Soft Computing area with recent trends of Distributed Artificial Intelligence and Ambient Int- ligence. We edited this book for two basic motivations: the emergence of computational intelligence as a mature and integrated science, and the power of the agent paradigm in realizing complex and distributed environments. Computational Intelligence has nowadays a mature formal theory and robust implementation experiences. Intelligent agents have shown their inner ability in acquiring data and knowledge from and performing appropriate de- sions through a functional relation that maps percepts to actions. Compu- tional Intelligence inside Agents are extremely helpful to support the designer of agent-based systems in treating with unknown or ill-defined envir- ments so to better handle knowledge representation and reasoning under these assumptions. The formulation of cooperative strategies, the linguistic aspects of the communication, the local decision versus the global kno- edge, the actions and plans as distributed viewpoints could be characterized by an incomplete knowledge and by a fragmentation of logical inter- tions, faced often by a non-monotonic reasoning. While building “smart” environments many challenges arises. The system needs to interact with its users in order to obtain feedback in a non-intrusive way. Explicit/implicit feedback is needed to take decisions in near-real time.

“Anything happens must have its own reason ”. Although I cannot really recall exactly when I heard of this statement for the first time, it is always in my mind and in fact it has been one of the motivations for me to carry out research and study. When I asked myself again about the purpose of writing this book at the time of writing this preface, several “add on” rea sons that had never occurred to me at the start of writing this book in the spring of 2003 surprisingly came up. Back then, when I was preparing the progress report for the iJADE (2. 0) project, a “fuzzy” idea of whether it was feasible to write a book on intelli gent agents came to my mind. This book not only would discuss and deal with the theory but also the “spin off” applications from the iJADE pro ject, including: the iJADE WeatherMan, the iJADE Stock Advisor, the iJADE Surveillant and the latest works on iJADE Negotiator. The fact that I had to launch the iJADE development kit officially over the Web in the summer of 2003 (http://www. ijadk. org) and to arrange courses and semi nars to teach and train our undergraduate students to make use of this tool kit further supported the idea and the future use of this book. Hence, the “archetype” of this book emerged.

The scope of this volume is to give to the reader a wide scenario of recent works characterized by a synergistic combination of Soft Computing area with recent trends of Distributed Artificial Intelligence and Ambient Int- ligence. We edited this book for two basic motivations: the emergence of computational intelligence as a mature and integrated science, and the power of the agent paradigm in realizing complex and distributed environments. Computational Intelligence has nowadays a mature formal theory and robust implementation experiences. Intelligent agents have shown their inner ability in acquiring data and knowledge from and performing appropriate de- sions through a functional relation that maps percepts to actions. Compu- tional Intelligence inside Agents are extremely helpful to support the designer of agent-based systems in treating with unknown or ill-defined envir- ments so to better handle knowledge representation and reasoning under these assumptions. The formulation of cooperative strategies, the linguistic aspects of the communication, the local decision versus the global kno- edge, the actions and plans as distributed viewpoints could be characterized by an incomplete knowledge and by a fragmentation of logical inter- tions, faced often by a non-monotonic reasoning. While building “smart” environments many challenges arises. The system needs to interact with its users in order to obtain feedback in a non-intrusive way. Explicit/implicit feedback is needed to take decisions in near-real time.

The Knowledge Seeker is a useful system to develop various intelligent applications such as ontology-based search engine, ontology-based text classification system, ontological agent system, and semantic web system etc. The Knowledge Seeker contains four different ontological components. First, it defines the knowledge representation model ¡V Ontology Graph. Second, an ontology learning process that based on chi-square statistics is proposed for automatic learning an Ontology Graph from texts for different domains. Third, it defines an ontology generation method that transforms the learning outcome to the Ontology Graph format for machine processing and also can be visualized for human validation. Fourth, it defines different ontological operations (such as similarity measurement and text classification) that can be carried out with the use of generated Ontology Graphs. The final goal of the KnowledgeSeeker system framework is that it can improve the traditional information system with higher efficiency. In particular, it can increase the accuracy of a text classification system, and also enhance the search intelligence in a search engine. This can be done by enhancing the system with machine processable ontology.

The Knowledge Seeker is a useful system to develop various intelligent applications such as ontology-based search engine, ontology-based text classification system, ontological agent system, and semantic web system etc. The Knowledge Seeker contains four different ontological components. First, it defines the knowledge representation model ¡V Ontology Graph. Second, an ontology learning process that based on chi-square statistics is proposed for automatic learning an Ontology Graph from texts for different domains. Third, it defines an ontology generation method that transforms the learning outcome to the Ontology Graph format for machine processing and also can be visualized for human validation. Fourth, it defines different ontological operations (such as similarity measurement and text classification) that can be carried out with the use of generated Ontology Graphs. The final goal of the KnowledgeSeeker system framework is that it can improve the traditional information system with higher efficiency. In particular, it can increase the accuracy of a text classification system, and also enhance the search intelligence in a search engine. This can be done by enhancing the system with machine processable ontology.