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The key issues of such a system are both a domain-independent reasoning algorithm and knowledge representation capabilities, specifically thought for both computation intensive and real-time RHM scenarios.
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The compelling feature of the approach is that it combines ontological and temporal knowledge representation formalisms to provide powerful representation capabilities for activity modelling.
The simulation results show the Neuro-fuzzy techniques have been more successful than conventional control because they combine the learning capabilities of neural network with the transparent Knowledge representation of fuzzy controller.
In the field of knowledge representation another important possibility deriving from the formal ontology application is its capability of solving ambiguity cases in human language.
It would be highly desirable to equip such CASE tools with automated reasoning capabilities, such as those studied in Artificial Intelligence and, in particular, in Knowledge Representation and Reasoning.
Professor Stuart Russell from U.C. Berkeley identifies six major capabilities that a computer needs to pass the Total Turing test: natural language processing, knowledge representation, automated reasoning, machine learning, computer vision and robotics.
Large-scale knowledge representation is another.
b Signal knowledge representation and a wavelet transform representation.
dynamics in knowledge representation and stream reasoning.
In summary, requirement R1 (flexible knowledge representation) and requirement R2 (expressive knowledge representation) are fulfilled.
The results suggest that knowledge representation is not simply a translation of acquired knowledge to a knowledge representation.
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