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The central problems in artificial intelligence include deduction, reasoning, problem solving, knowledge representation, and learning [17].
This is mainly due to the fact that most existing systems have been designed with focus on technology only [16], while they should equally address knowledge representation and learning strategies' aspects for mobile learning.
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These methods will depend on advances in many areas such as statistics, knowledge representation and ontology, machine learning, data mining, graph theory, and visualization.
Integrated neuro-fuzzy systems can unite with the human-like knowledge representation and clarification abilities of fuzzy systems with the parallel calculation and learning abilities of neural networks (Jang and Sun 1995).
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.
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.
These activities are: knowledge creation, knowledge sharing, and knowledge representation and retrieval.
This chapter emphasizes the importance of knowledge representation and fusion.
An ontology has been designed for knowledge representation and management.
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