Book Chapter10.1007/978-3-540-71270-1_10
Functional knowledge exchange within an intelligent distributed system
Oliver Buchtala,Bernhard Sick +1 more
- 12 Mar 2007
- pp 126-141
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TL;DR: An architecture of so-called organic nodes that face a classification problem is presented, showing 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 over-all system.
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Abstract: Humans learn from other humans - and intelligent nodes of a distributed system operating in a dynamic environment (eg, 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 knowledgeWe 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 over-all 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
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Citations
Quantitative Emergence -- A Refined Approach Based on Divergence Measures
Dominik Fisch,Martin Jänicke,Bernhard Sick,Christian Müller-Schloer +3 more
- 27 Sep 2010
TL;DR: This article shows how emergence in technical systems can be detected and measured gradually using techniques from the field of probability theory and information theory, and proposes emergence measures that are well-suited for continuous attributes using either non-parametric or model-based probability density estimation techniques.
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•Journal Article
Emergence in organic computing systems : Discussion of a controversial concept
TL;DR: In this paper, some very recent approaches for definitions of emergence in more or less technical contexts are discussed from the viewpoint of organic computing and some new thoughts that may help to come to a unifying notion of emergence for intelligent technical systems.
32
Learning from others: Exchange of classification rules in intelligent distributed systems
TL;DR: Methods for knowledge acquisition in dynamic environments, for gathering and using meta-knowledge about rules (i.e., experience), and for rule exchange in distributed systems are introduced based on a probabilistic knowledge modeling approach.
23
Techniques for knowledge acquisition in dynamically changing environments
Dominik Fisch,Martin Jänicke,Edgar Kalkowski,Bernhard Sick +3 more
- 04 May 2012
TL;DR: This article proposes and investigates new techniques for knowledge acquisition by novelty detection and reaction as well as obsoleteness detection and response that an agent may use for self-adaptation to new situations.
22
Technical data mining with evolutionary radial basis function classifiers
Markus Bauer,Oliver Buchtala,Timo Horeis,Ralf Kern,Bernhard Sick,Robert Wagner +5 more
- 01 Mar 2009
TL;DR: It is shown how an evolutionary algorithm can be used to optimize radial basis function (RBF) neural networks used for classification tasks and how appropriate training algorithms for RBF networks and penalty terms in the fitness function of the EA may improve the understandability of the extracted rules.
18
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