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Incremental Learning from Distributed Dynamic Data Sources

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Description: Multi-agent systems for incremental data-driven knowledge acquisition from distributed data sources.
Artificial Intelligence Research Laboratory Incremental Learning From Distributed, Dynamic Data Sources Mr. Carson Andorf, M.S. Student. Translating recent advances in high throughput data acquisition and storage technologies and networks into fundamental gains in understanding of respective domains (e.g., in biological sciences, organizational decision support) call for the development of powerful new tools for knowledge acquisition. For example, by examining data gathered by sensors located at different network hosts (e.g., system logs that contain records of various system calls) and known cases of coordinated attacks on the network, a knowledge acquisition agent can infer useful, a-priori unknown predictive relationships that can be subsequently employed for predicting, detecting, and counteracting intrusions. Similarly, bioinformatics knowledge discovery agents can learn regularities that characterize molecular structure-function relationships. The acquired knowledge, in addition to being of immediate value to the users, would also be used by software agents to hypothesize likely events based on information available and then seek out additional data to support or refute the hypothesis (e.g., in the context of data-driven scientific discovery).
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Page title:Distributed Knowledge Networks
Keywords:Artificial Intelligence, Cognitive Science, Neural Networks, Autonomous Robots, Automata Induction, Computational Learning Theory, Computational Organizational Theory, Data Mining, Knowledge Discovery, Visualization, Decision Support Systems, Distributed Knowledge Networks, Intelligent Agents, Mobile Agents, and Multi-Agent Systems, Knowledge Representation, Exploratory Data Analysis, Inference, Machine Learning, Parallel and Distributed AI, Biological Computation, Computational Neuroscience; Evolutionary, Cellular, and Neural Computation, Bioinformatics, Computational Biology, Genetic Regulatory Networks, Protein Structure-Function Prediction, Computational Genomics, Metabolic Pathways, Distributed Knowledge Networks for Bioinformatics, Complex Adaptive Systems, Network Information Systems, Distributed Knowledge Networks, Distributed Databases, Mediators, Data Warehouses, Intelligent Agents, Mobile Agents, Multi-Agent Systems, Diagnosis, Complex Systems Monitoring and Control, Intrusion Detection, honavar, vasant honavar
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