Rule-based System: What & Why

Summary

This video introduces the concept of rule-based systems (RBS), explaining their structure, importance, and practical applications. It covers key components such as preconditions (left-hand side) and actions (right-hand side), as well as the forward and backward chaining inference methods. RBS is presented as a widely used approach in real-world applications, such as expert systems and hybrid systems. The video also demonstrates how RBS processes facts in working memory through examples in the Clips programming language, helping viewers understand the syntax, architecture, and how the system reaches conclusions.

Takeaways

• 😀 Rule-based systems (RBS) are a type of expert system that use knowledge represented in the form of rules with preconditions and actions.
• 😀 The system consists of two key parts: the precondition (left-hand side) and the action (right-hand side). Actions are only executed if the preconditions are met.
• 😀 RBS is widely used in real-world applications, such as diagnostic systems for diseases like coronavirus and hybrid systems that combine rule-based and neural networks.
• 😀 An example of a rule syntax is presented in the CLIPS system, where rules are defined with a specific structure, including conditions and actions.
• 😀 RBS architecture includes production memory (for storing rules), working memory (for facts), and an inference engine (for matching patterns between facts and rules).
• 😀 The inference engine uses pattern matching to draw conclusions based on facts in the working memory and the rules in the production memory.
• 😀 The script demonstrates an example where the inference engine processes facts to infer new facts, such as identifying a desert as valuable.
• 😀 Forward chaining, which is data-driven, matches facts to the left-hand side of the rules and executes actions accordingly.
• 😀 Backward chaining, which is goal-driven, matches facts to the right-hand side of the rules and tries to satisfy a goal by working backwards.
• 😀 The script describes how facts are processed in cycles and how rules are applied iteratively to generate conclusions.
• 😀 In the next video, forward chaining and backward chaining will be explored in more detail, helping to understand their differences and applications.

Q & A

What is a Ruby-based system and why is it important to study?

A Ruby-based system is a type of expert system that uses a set of predefined rules to make inferences based on input data. It is important to study because it is a simple and widely used approach in real-world applications, such as expert systems, signal classification, and hybrid systems that combine rule-based systems with neural networks.

What are the two main parts of a rule in a Ruby-based system?

A rule in a Ruby-based system consists of two parts: the precondition (or left-hand side) and the action (or right-hand side). The action is executed only if the precondition is satisfied.

How is rule matching performed in a Ruby-based system?

Rule matching is done by the inference engine, which compares the facts in the working memory with the conditions (left-hand side) of the rules. When a match is found, the corresponding actions (right-hand side) are executed.

What is the general architecture of a Ruby-based system?

The architecture of a Ruby-based system consists of three main components: production memory (which stores rules), working memory (which stores facts), and the inference engine (which matches patterns between facts and rule conditions).

What is the difference between forward chaining and backward chaining?

Forward chaining is a data-driven approach where the system starts with known facts and applies rules to infer new facts. Backward chaining is goal-driven, where the system starts with a goal and works backward to determine the facts needed to achieve that goal.

What is the syntax of a rule in a Ruby-based system using Clips?

In Clips, the syntax for defining a rule is: `(defrule rule-name (condition) (action))`. The condition specifies the facts that must be true for the rule to fire, and the action defines what happens when the rule is triggered.

What does the Clips system use to add facts to the working memory?

Clips uses the `assert` command to add facts to the working memory. Facts can be added one by one or in bulk using a predefined set of facts.

What is the purpose of the inference engine in a Ruby-based system?

The inference engine's purpose is to evaluate the conditions of the rules, match them with the facts in the working memory, and trigger the appropriate actions when conditions are met.

How are inference results represented in a Ruby-based system?

Inference results are represented as new facts that are added to the working memory. These facts are the outcome of applying rules based on the initial facts and the system's knowledge base.

What is an example of a practical application of a Ruby-based system?

An example of a practical application of a Ruby-based system is an expert system for diagnosing diseases or conditions based on symptoms. The system uses predefined rules to infer diagnoses based on the input symptoms, similar to how the Clips tool is used in the video for rule-based inference.