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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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"text": "Rule-based systems are also known as production systems or expert systems."
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"text": "1. Derivation or Deduction Rules.
2. Transformation Rules.
3. Integrity Constraints.
4. Reaction or Event-Condition-Action (ECA) Rules"
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"text": "1. Automated Theorem Provers.
2. Logic Programming.
3. Production Systems.
4. Modern Reasoners."
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"text": "In a rule-based language, programs consist of a set of rules. Each rule has two parts, called the head that represents the consequences and the body that represents premises, respectively. A rule-based system attempts to derive execution instructions from a starting set of data and rules."
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"text": "Rule-based classifiers are just another type of classifiers that make the class decision depending on using various “if /then” rules. These rules are easily interpretable and thus these classifiers are generally used to generate descriptive models. The condition used with “if” is called the antecedent and the predicted class of each rule is called the consequent."
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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 collaborativeMethodologyA 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:
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.
A typical rule-based system has four basic components:
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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