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Compute-intensive methods for knowledge representation and reasoning.
Logic-based methods for knowledge representation, information change, and games in artificial intelligence and philosophy.
Methods for knowledge initialization, knowledge refinement and knowledge extraction are introduced.
Special focus is placed on advanced methods for knowledge discovery such as high-throughput kinetic modeling and QSAR.
For that purpose, we introduce 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.
Some fields of application of AI are automatic problem solving methods for knowledge representation and knowledge engineering, machine vision and pattern recognition, artificial learning, automatic programming, the theory of games, and so forth.
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Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences.
The combination of HRS with ANNs appeared to be an excellent method for knowledge extraction.
Publishing in peer review journals is recognised as the main method for knowledge diffusion: nevertheless publication efficiency has received little attention to date.
This ontology, beyond the clarification it brings to the involved notions, will be the support of a software architecture for implementing the method for knowledge acquisition and problem formulation.
In this article, we discuss the importance of postmortem reviews as a method for knowledge sharing in software projects, and give an overview of known such processes in the field of software engineering.
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means for knowledge
approach for knowledge
methods for calculation
methods for reclamation
methods for explanation
methods for everyone
methods for concentration
methods for inducing
methods for detail
methods for definition
methods for domain
methods for information
methods for understanding
method for knowledge
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