1. Knowledge Acquisition
Gathering data and information from various sources, including databases, sensors and human input
2. Knowledge Representation
Organizing and structuring this knowledge using techniques like ontologies and semantic networks for effective processing
3. Knowledge Utilization
Applying the structured knowledge to perform tasks, make decisions and solve problems through reasoning and inference
4. Knowledge Learning
Continuously updating the knowledge base by learning from new data and outcomes using machine learning algorithms.
5. Knowledge Validation and Verification
Ensuring the accuracy, consistency and reliability of the knowledge through validation against real-world outcomes.
6. Knowledge Maintenance
Regularly updating the knowledge base to stay relevant and accurate as the environment of information changes
7. Knowledge Sharing
Distributing the knowledge to other systems or users, making it accessible and usable beyond the original AI system.