Mohammed NaserKnowledge-based systems are a form of AI tech that imbibes human expertise and knowledge to aid decision-making processes. Read on.
Knowledge-based systems can differ in their problem-solving approaches or ways. While some systems use expert knowledge as rules and are known as rule-based systems, other systems encode case-based reasoning where they substitute cases for regulations. These cases ultimately solve current problems that case-based systems will attempt to apply to rectify new issues.
But, what exactly are knowledge-based systems? Here is a glance.
What are Knowledge-Based Systems?
Knowledge-based systems, also known by the acronym KBS, are a form of artificial intelligence technology that looks to imbibe the knowledge from human expertise to aid decision-making processes. Some examples of knowledge-based systems include expert systems, which are referred to as such because they rely on human expertise for their functions.
Normally, the architecture of knowledge-based systems, informing its problem-solving methods, has a knowledge base and an inference engine. Within this, the knowledge base includes a collection of information in a given field, such as medical diagnosis. Further, the inference engine deduces insights from the data stored in the knowledge base. KBS also has an interface whereby users query the system and perform interactions.
Use Cases
Over the past years, knowledge-based systems have risen in popularity and developed for several applications. For example, MYCIN was an early KBS crafted to aid doctors in diagnosing diseases better. The healthcare industry is a prominent area of operation for KBS. Within the industry and in a health sciences context, KBS is often referred to as clinical decision-support systems. Knowledge-based systems have also been used in a wide range of applications such as ash management, avalanche path analysis, and industrial equipment fault diagnosis.
KBS and AI
Even while being a part of the artificial intelligence umbrella, classical knowledge-based systems vary significantly in approach to some of the recent AI growth. AI has changed from a top-down-organized and efficient entity to a bottom-up method. It helps systems harness Big Data aiding processes like deep learning and data mining. Neural network systems are an example of this new phenomenon.
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