Knowledge-Based Systems and Expert Systems in 2025
Key points:
Knowledge-based systems and expert systems focus on reasoned decision-making using structured knowledge, facts, rules, and logical inference.
These systems typically consist of three components: a knowledge base (storing facts and rules), an inference engine (for reasoning), and a user interface.
Modern systems have evolved from static rule-based systems to more dynamic ones that can learn and update their knowledge.
Key recent developments include integration with generative AI, agentic AI, and spatial computing (VR/AR).
Applications include medicine (diagnosis), military (Aegis Combat System), business operations, and customer experiences.
Rules typically follow an “if-then” structure and can be organized as ordered lists or unordered sets.
Inference engines use strategies like forward chaining (data-driven) or backward chaining (goal-driven).
Unlike traditional programming, KBS separates knowledge from control logic, making modifications easier.
Knowledge acquisition remains challenging, often requiring 60-70% of development effort.
AutoML focuses on automating the machine learning pipeline, improving efficiency and accessibility.
The key difference is KBS uses explicit knowledge representation while ML embeds implicit knowledge in model parameters.
Hybrid approaches combining rule-based reasoning with machine learning show significant promise.
Both approaches have their place in modern AI, with KBS valued for explainability and structured reasoning, while ML excels at pattern recognition in large datasets.