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Rule-based systems in machine learning refer to a type of artificial intelligence (AI) approach that makes use of explicitly defined rules to make decisions or perform tasks. These systems rely on a set of predefined rules and logic to process input data and produce output. Each rule typically consists of a condition and an associated action. 

The main components of rule-based systems are: 

i) Rule Base: 

A collection of rules is established, where each rule comprises a condition and an action. The condition specifies a set of criteria or constraints based on input data, and the action represents the response or decision to be taken when the condition is met. 

ii) Inference Engine: 

The inference engine is responsible for applying the rules to the input data. It evaluates the conditions of each rule against the current input and determines which rules are satisfied. 

iii) Decision Making: 

Based on the satisfied rules, the system makes decisions or performs actions. This process is often deterministic, meaning that the output is directly determined by the rules and input data. 

Rule-based systems have some advantages, including transparency (as the rules are explicit and interpretable), ease of knowledge representation, and relatively simple maintenance. However, they may struggle with handling uncertainty or adapting to complex and dynamic environments, as they depend on predefined rules. Expert systems, a specific type of rule-based system, are designed to emulate the decision-making abilities of a human expert in a particular domain. They gained popularity in the early days of AI but have been largely supplanted by more data-driven approaches, such as machine learning algorithms, in many applications. 

In contrast to machine learning models that learn patterns from data, rule-based systems are based on human expertise and explicit knowledge. While rule-based systems can be effective in certain well-defined domains, they may not be as flexible or adaptive as machine learning models in handling diverse and evolving data. 

The script defines a rule-based system, which allows the user to define rules and apply them to a set of observations. Script explanation: 

add_rule method: Takes a condition and a conclusion as parameters and appends them as a tuple to the rules list. 

apply_rules method: Takes a dictionary of observations as a parameter and iterates through the stored rules. If all conditions specified in a rule are satisfied based on the observations, it returns the corresponding conclusion. If no rule is satisfied, it returns None. 

Application of Rules: It defines rules using the add_rule method.

Output of script execution:

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