Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
The Vision of Autonomic Computing
Computer
Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer
RoboCup 2001: Robot Soccer World Cup V
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Organic Computing - Addressing Complexity by Controlled Self-Organization
ISOLA '06 Proceedings of the Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation
Emergence in organic computing systems: discussion of a controversial concept
ATC'06 Proceedings of the Third international conference on Autonomic and Trusted Computing
Technical data mining with evolutionary radial basis function classifiers
Applied Soft Computing
Techniques for knowledge acquisition in dynamically changing environments
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
Learning from others: Exchange of classification rules in intelligent distributed systems
Artificial Intelligence
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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 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.