Knowledge Engineering
Knowledge engineering is a field within artificial intelligence that focuses on the development and use of knowledge-based systems. It involves the process of creating, structuring, and implementing the knowledge that these systems need to operate effectively.
This includes the acquisition of knowledge from domain experts or other sources, its conceptualization and formalization into a machine-interpretable format, and the design of algorithms and architectures that can utilize this knowledge for reasoning, decision-making, and problem-solving.
Knowledge engineering encompasses various activities such as knowledge elicitation, modeling, representation, and validation, ensuring the system's knowledge base is accurate, efficient, and reliable. It requires a multidisciplinary approach, combining insights from computer science, cognitive science, and domain-specific expertise.
In the development of an expert system for medical diagnosis, knowledge engineering would involve gathering detailed medical knowledge from physicians, textbooks, and clinical guidelines. This knowledge would then be structured into a format suitable for the expert system, such as a set of diagnostic rules or a decision tree.
The system would use this structured knowledge to infer diagnoses based on symptoms, medical history, and test results provided by users. Another example is the creation of a financial advice system, where knowledge engineering would involve encoding investment strategies, market analysis techniques, and regulatory compliance rules into the system, enabling it to provide personalized financial advice to users based on their financial data and goals.