Catálogo de publicaciones - libros

Humans learn from other humans – and intelligent nodes of a distributed system operating in a dynamic environment (e.g., robots, smart sensors, or software agents) should do the same! Humans do not only learn by communicating facts but also by exchanging rules. The latter can be seen as a more generic, abstract kind of knowledge. We refer to these two kinds of knowledge as “descriptive” and “functional” knowledge, respectively. In a dynamic environment, where new knowledge arises or old knowledge becomes obsolete, intelligent nodes must adapt on-line to their local environment by means of self-learning mechanisms. If they exchange functional knowledge in addition to descriptive knowledge, they will efficiently be enabled to cope with a particular phenomenon before they observe this phenomenon in their local environment, for instance. In this article, we present an architecture of so-called organic nodes that face a classification problem. We show how a need for new functional knowledge is detected, how new rules are determined, and how the exchange of locally acquired rules within a network of organic nodes leads to a certain kind of self-optimization of the overall system. We show the potential of our methods using an artificial scenario and a real-world scenario from the field of intrusion detection in computer networks.

Humans learn from other humans – and intelligent nodes of a distributed system operating in a dynamic environment (e.g., robots, smart sensors, or software agents) should do the same! Humans do not only learn by communicating facts but also by exchanging rules. The latter can be seen as a more generic, abstract kind of knowledge. We refer to these two kinds of knowledge as “descriptive” and “functional” knowledge, respectively. In a dynamic environment, where new knowledge arises or old knowledge becomes obsolete, intelligent nodes must adapt on-line to their local environment by means of self-learning mechanisms. If they exchange functional knowledge in addition to descriptive knowledge, they will efficiently be enabled to cope with a particular phenomenon before they observe this phenomenon in their local environment, for instance. In this article, we present an architecture of so-called organic nodes that face a classification problem. We show how a need for new functional knowledge is detected, how new rules are determined, and how the exchange of locally acquired rules within a network of organic nodes leads to a certain kind of self-optimization of the overall system. We show the potential of our methods using an artificial scenario and a real-world scenario from the field of intrusion detection in computer networks.

Humans learn from other humans – and intelligent nodes of a distributed system operating in a dynamic environment (e.g., robots, smart sensors, or software agents) should do the same! Humans do not only learn by communicating facts but also by exchanging rules. The latter can be seen as a more generic, abstract kind of knowledge. We refer to these two kinds of knowledge as “descriptive” and “functional” knowledge, respectively. In a dynamic environment, where new knowledge arises or old knowledge becomes obsolete, intelligent nodes must adapt on-line to their local environment by means of self-learning mechanisms. If they exchange functional knowledge in addition to descriptive knowledge, they will efficiently be enabled to cope with a particular phenomenon before they observe this phenomenon in their local environment, for instance. In this article, we present an architecture of so-called organic nodes that face a classification problem. We show how a need for new functional knowledge is detected, how new rules are determined, and how the exchange of locally acquired rules within a network of organic nodes leads to a certain kind of self-optimization of the overall system. We show the potential of our methods using an artificial scenario and a real-world scenario from the field of intrusion detection in computer networks.

Humans learn from other humans – and intelligent nodes of a distributed system operating in a dynamic environment (e.g., robots, smart sensors, or software agents) should do the same! Humans do not only learn by communicating facts but also by exchanging rules. The latter can be seen as a more generic, abstract kind of knowledge. We refer to these two kinds of knowledge as “descriptive” and “functional” knowledge, respectively. In a dynamic environment, where new knowledge arises or old knowledge becomes obsolete, intelligent nodes must adapt on-line to their local environment by means of self-learning mechanisms. If they exchange functional knowledge in addition to descriptive knowledge, they will efficiently be enabled to cope with a particular phenomenon before they observe this phenomenon in their local environment, for instance. In this article, we present an architecture of so-called organic nodes that face a classification problem. We show how a need for new functional knowledge is detected, how new rules are determined, and how the exchange of locally acquired rules within a network of organic nodes leads to a certain kind of self-optimization of the overall system. We show the potential of our methods using an artificial scenario and a real-world scenario from the field of intrusion detection in computer networks.

Humans learn from other humans – and intelligent nodes of a distributed system operating in a dynamic environment (e.g., robots, smart sensors, or software agents) should do the same! Humans do not only learn by communicating facts but also by exchanging rules. The latter can be seen as a more generic, abstract kind of knowledge. We refer to these two kinds of knowledge as “descriptive” and “functional” knowledge, respectively. In a dynamic environment, where new knowledge arises or old knowledge becomes obsolete, intelligent nodes must adapt on-line to their local environment by means of self-learning mechanisms. If they exchange functional knowledge in addition to descriptive knowledge, they will efficiently be enabled to cope with a particular phenomenon before they observe this phenomenon in their local environment, for instance. In this article, we present an architecture of so-called organic nodes that face a classification problem. We show how a need for new functional knowledge is detected, how new rules are determined, and how the exchange of locally acquired rules within a network of organic nodes leads to a certain kind of self-optimization of the overall system. We show the potential of our methods using an artificial scenario and a real-world scenario from the field of intrusion detection in computer networks.