Introduction

In this tutorial, we will explore the concept of Expert Systems, a prominent branch of artificial intelligence. We aim to provide a comprehensive understanding of their purpose, components, and how they function.

By the end of this tutorial, you will learn:
- What is an Expert System?
- The components of an Expert System
- How Expert Systems function
- How to build a simple Expert System

Prerequisites: Basic knowledge of programming and understanding of Artificial Intelligence.

Step-by-Step Guide

What is an Expert System?

An expert system is a computer system that emulates the decision-making ability of a human expert. It is designed to solve complex problems by reasoning about knowledge, represented mainly as if–then rules rather than conventional procedural code.

Components of an Expert System

1. Knowledge Base: It is the storehouse of knowledge that the system uses to solve problems. This knowledge is high-level and detailed, representing the human expert's understanding of a particular problem domain.

2. Inference Engine: This is the 'brain' of the system. It applies logical rules to the knowledge base to derive answers from it.

3. User Interface: This is the system component that allows a user to interact with the system, input data, and receive responses.

How Expert Systems Function

An expert system functions in a cycle of four primary stages:

• User Input: The user inputs a query into the system.
• Inference Engine: The system processes the query, applying rules and knowledge from the knowledge base.
• Knowledge Base Consultation: The system refers to the knowledge base to find relevant responses.
• Response: The system generates and provides an output to the user.

Code Examples

Since Expert Systems are usually large and complex, we will illustrate with a simple rule-based system using Python.

# import necessary modules
from experta import Fact, Rule, KnowledgeEngine

# Define a fact (an assertion that can be made in the system)
class Light(Fact):
    pass

# Create the expert system
class LightExpert(KnowledgeEngine):
    @Rule(Light(color='red'))
    def rule_red(self):
        print("Stop")

    @Rule(Light(color='green'))
    def rule_green(self):
        print("Go")

# Create an instance of the expert system
engine = LightExpert()

# Assert a new fact
engine.declare(Light(color='red'))

# Run the expert system
engine.run()

This code represents a very basic expert system that decides whether to "go" or "stop" based on a light's color. The output for the code would be "Stop".

Summary

In this tutorial, you learned about Expert Systems, their purpose, components, and functioning. The next step would be to delve deeper into the creation of more complex systems, involving a larger knowledge base and more intricate rules.

For further reading, you can refer to "Artificial Intelligence: Structures and Strategies for Complex Problem Solving" by George F. Luger.

Practice Exercises

1. Modify the LightExpert system to include a rule for the color 'yellow'.
2. Create a simple Expert System that recommends an outfit based on the weather.

Solutions:

@Rule(Light(color='yellow'))
def rule_yellow(self):
    print("Wait")

class Weather(Fact):
    pass

class OutfitExpert(KnowledgeEngine):
    @Rule(Weather(condition='rainy'))
    def rule_rainy(self):
        print("Wear a raincoat")

    @Rule(Weather(condition='sunny'))
    def rule_sunny(self):
        print("Wear sunglasses")

engine = OutfitExpert()
engine.declare(Weather(condition='rainy'))
engine.run()

Keep practicing with different scenarios and rules to get a better understanding of Expert Systems.