A knowledge-based system (KBS) is a form of artificial intelligence (AI) that aims to capture the knowledge of human experts to support decision-making. Examples of knowledge-based systems include expert systems, which are so called because of their reliance on human expertise.Content Continues Below
The typical architecture of a knowledge-based system, which informs its problem-solving method, includes a knowledge base and an inference engine. The knowledge base contains a collection of information in a given field -- medical diagnosis, for example. The inference engine deduces insights from the information housed in the knowledge base. Knowledge-based systems also include an interface through which users query the system and interact with it.
A knowledge-based system may vary with respect to its problem-solving method or approach. Some systems encode expert knowledge as rules and are therefore referred to as rule-based systems. Another approach, case-based reasoning, substitutes cases for rules. Cases are essentially solutions to existing problems that a case-based system will attempt to apply to a new problem.
Where knowledge-based systems are used
Over the years, knowledge-based systems have been developed for a number of applications. MYCIN, for example, was an early knowledge-based system created to help doctors diagnose diseases. Healthcare has remained an important market for knowledge-based systems, which are now referred to as clinical decision-support systems in the health sciences context.
Knowledge-based systems have also been employed in applications as diverse as avalanche path analysis, industrial equipment fault diagnosis and cash management.
Knowledge-based systems and artificial intelligence
While a subset of artificial intelligence, classical knowledge-based systems differ in approach to some of the newer developments in AI.
Daniel Dennett, a philosopher and cognitive scientist, in his 2017 book, From Bacteria to Bach and Back, cited a strategy shift from early AI, characterized by "top-down-organized, bureaucratically efficient know-it-all" systems to systems that harness Big Data and "statistical pattern-finding techniques" such as data-mining and deep learning in a more bottom-up approach.
Examples of AI following the latter approach include neural network systems, a type of deep-learning technology that concentrates on signal processing and pattern recognition problems such as facial recognition.