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Techniques for Knowledge Acquisition in Dynamically Changing Environments DOMINIK FISCH, MARTIN JANICKE, EDGAR KALKOWSKI, and BERNHARD SICK, University of Kassel 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 time novel rules are required or old rules become obsolete. In this article we propose and investigate new techniques for knowledge acquisition by novelty detection and reaction as well as obsoleteness detection and reaction that an agent may use for self-adaptation to new situations. For that purpose we consider classi ers based on probabilistic rules. Premises of new rules are learned autonomously while conclusions are either obtained from human experts or from other agents which have learned appropriate rules in the past. By means of knowledge exchange, agents will ef ciently be enabled to cope with situations they were not confronted with before. This kind of collaborative intelligence follows the human archetype: Humans are able to learn from each
ACM Transactions on Autonomous and Adaptive Systems (TAAS) – Association for Computing Machinery
Published: Apr 1, 2012
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