Expert systems are AI systems that rely on a knowledge base and a set of rules to draw conclusions and make decisions. These systems often use logic-based knowledge representations to encode facts, rules, and axioms about a specific domain. Knowledge representation is the process of structuring and organizing information so that an intelligent agent can effectively use it to draw inferences, solve problems, and determine actions. Common methods of knowledge representation include semantic networks, frames, and logic-based approaches.
The difference between symbolic AI and modern machine learning (ML) lies in their approach to problem-solving. Symbolic AI is based on explicitly coded rules and logical structures that allow for precise and understandable conclusions but often struggle with ambiguous or complex real-world data. In contrast, modern ML relies on statistical models that identify and utilize patterns in large datasets, though they are often less explainable and have difficulties with exact computations and verifiable reasoning. Modern approaches often combine both methods to leverage the strengths of each paradigm.