Which category of problems do expert systems primarily address?

Expert systems primarily address classification problems. They are designed to mimic human expertise in specific domains, allowing them to analyze data and categorize information based on pre-established rules and knowledge. By leveraging a knowledge base, which contains domain-specific information and inference rules, expert systems can draw conclusions and make classifications about new data inputs.

This classification capability is particularly useful in fields such as medical diagnosis, where an expert system might classify patient symptoms to provide suggestions for potential conditions or treatments. The system evaluates input data against its knowledge base and identifies which category or classification the information fits within.

The other options, while relevant to various aspects of information technology and artificial intelligence, do not directly encapsulate the primary function of expert systems. For example, policy development suggests a broader decision-making framework that may involve more subjective judgment, while logic and control pertain to navigating processes but not specifically to categorizing data. High complexity refers to the nature of certain problems that may require expert systems but doesn’t define what expert systems do best, which is classifying and reasoning based on a specific set of rules and information.