knowledge-based systems (KBS)

Contributor(s): John Moore

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.

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.

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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.

Diagram showing the basic architecture of a knowledge-based system
Knowledge-based systems represent a rules-based or case-based approach to AI

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.

This was last updated in May 2018

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Where do you see applications for knowledge-based systems?
I am no expert but i can think of few possibilities : 

1. Imagine a world infiltrated by IoT. Nearly every device connected to the internet and can query a universal KBS to perform simple to complex task with little to no intervention of humans. 

2. KBS could also be used to deduce clues from a crime scene or even assist investigation.

3. Product recommendations based on more variables that are related to application, not just from looking at the data trends of what others bought.

4. Deduce strengths and weakness of individual students and recommend personalized curriculum for them.

I actually believe that the use of KBS could quite possible become as common as we use social media today...

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