Edgar Kalkowski

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Intelligent agents often have the same or similar tasks and sometimes they cooperate to solve a given problem. These agents typically know how to observe their local environment and how to react on certain observations, for instance, and this knowledge may be represented in form of rules. However, many environments are dynamic in the sense that from time to(More)
If knowledge such as classification rules are extracted from sample data in a distributed way, it may be necessary to combine or fuse these rules. In a conventional approach this would typically be done either by combining the classifiers' outputs (e.g., in form of a classifier ensemble) or by combining the sets of classification rules (e.g., by weighting(More)
“Learning by doing” and “learning by teaching” are two important concepts for human education. In this article, we demonstrate that these learning concepts can also be realized by intelligent, so-called organic computing systems. These organic agents either improve their skills by themselves, eventually assisted by a teacher, or(More)
After data selection, pre-processing, transformation, and feature extraction, knowledge extraction is not the final step in a data mining process. It is then necessary to understand this knowledge in order to apply it efficiently and effectively. Up to now, there is a lack of appropriate techniques that support this significant step. This is partly due to(More)
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