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     "text": "A rule-based system is a system that's applied on semi-synthetic rules to store and manipulate information. In doing so, it tries to repeat human intelligence. In terms of working, rule-based systems need a collection of facts or a supply of knowledge and a collection of rules for manipulating that information. These rules are typically named as ‘If statements’ as they have a tendency to usually follow the logic of ‘IF X happens THEN do Y’.r Check"
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2. Transformation Rules.
3. Integrity Constraints.
4. Reaction or Event-Condition-Action (ECA) Rules"
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Rule-Based System

What is a rule-based system?

A rule-based system is a system that applies human-made rules to store, sort and manipulate data. In doing so, it mimics human intelligence.

To work, rule-based systems require a set of facts or source of data, and a set of rules for manipulating that data. These rules are sometimes referred to as ‘If statements’ as they tend to follow the line of ‘IF X happens THEN do Y’. 

The steps can be simplified to:

  • First comes the data or new business event
  • Then comes the analysis: the part where the system conditionally processes the data against its rules
  • Then comes any subsequent automated follow-up actions

How does a rule-based system work?

Rule-based systems, unsurprisingly, work based on rules. These rules outline triggers and the actions that should follow (or are triggered). For example, a trigger might be an email containing the word “invoice”. An action might then be to forward the email to the finance team.

These rules most often take the form of if statements. ‘IF’ outlines the trigger, ‘THEN’ specifies the action to complete. So, if you want to create a rule-based system capable of handling 100 different actions, you’d have to write 100 different rules. If you want to then update the system and add actions, then you would need to write new rules.

In short, you use rules to tell a machine what to do, and the machine will do exactly as you tell it. From there, rule-based systems will execute the actions until you tell it to stop.

But remember: if you tell it to do something incorrectly, it will do it incorrectly.

Components of a rules-based system

A typical rule-based system has four basic components:

  • A list of rules or rule base, which is a specific type of knowledge base.
  • An inference engine or semantic reasoner, which infers information or takes action based on the interaction of input and the rule base. 
  • Temporary working memory.
  • A user interface or other connection to the outside world through which input and output signals are received and sent.

How are rules-based systems different from learning-based systems?

In contrast to Rules-Based Systems, Learning Systems observe data and continuously learn from it.

That is, while Rules-Based Systems quickly become out of date, Learning Systems automatically improve over time. And Learning Systems improve without the massive expense of having people maintain complex rules.

Learning systems find patterns and treat similar things similarly. Think of how Netflix or Hulu keep track and learn from what you watch, and then compare it to what people like you watch to make recommendations and keep you binge-watching. There is some complexity in execution, but it's a much simpler concept. And a concept more in line with how humans also learn.

Finally, Learning Systems' results are necessarily more measurable. It's the only way they can improve over time; because if they cannot measure results, they could not learn what actions are better and what actions are worse.


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Rule-Based System

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is a rule-based system?

A rule-based system is a system that applies human-made rules to store, sort and manipulate data. In doing so, it mimics human intelligence.

To work, rule-based systems require a set of facts or source of data, and a set of rules for manipulating that data. These rules are sometimes referred to as ‘If statements’ as they tend to follow the line of ‘IF X happens THEN do Y’. 

The steps can be simplified to:

  • First comes the data or new business event
  • Then comes the analysis: the part where the system conditionally processes the data against its rules
  • Then comes any subsequent automated follow-up actions

How does a rule-based system work?

Rule-based systems, unsurprisingly, work based on rules. These rules outline triggers and the actions that should follow (or are triggered). For example, a trigger might be an email containing the word “invoice”. An action might then be to forward the email to the finance team.

These rules most often take the form of if statements. ‘IF’ outlines the trigger, ‘THEN’ specifies the action to complete. So, if you want to create a rule-based system capable of handling 100 different actions, you’d have to write 100 different rules. If you want to then update the system and add actions, then you would need to write new rules.

In short, you use rules to tell a machine what to do, and the machine will do exactly as you tell it. From there, rule-based systems will execute the actions until you tell it to stop.

But remember: if you tell it to do something incorrectly, it will do it incorrectly.

Components of a rules-based system

A typical rule-based system has four basic components:

  • A list of rules or rule base, which is a specific type of knowledge base.
  • An inference engine or semantic reasoner, which infers information or takes action based on the interaction of input and the rule base. 
  • Temporary working memory.
  • A user interface or other connection to the outside world through which input and output signals are received and sent.

How are rules-based systems different from learning-based systems?

In contrast to Rules-Based Systems, Learning Systems observe data and continuously learn from it.

That is, while Rules-Based Systems quickly become out of date, Learning Systems automatically improve over time. And Learning Systems improve without the massive expense of having people maintain complex rules.

Learning systems find patterns and treat similar things similarly. Think of how Netflix or Hulu keep track and learn from what you watch, and then compare it to what people like you watch to make recommendations and keep you binge-watching. There is some complexity in execution, but it's a much simpler concept. And a concept more in line with how humans also learn.

Finally, Learning Systems' results are necessarily more measurable. It's the only way they can improve over time; because if they cannot measure results, they could not learn what actions are better and what actions are worse.


Thanks for reading! We hope you found this helpful.

Ready to level-up your business? Click here.

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