Introduction to Expert Systems

Introduction

Expert systems represent early symbolic AI. They use rules and facts to solve domain problems. The logic stays strict. The workflow stays predictable. Next, you discover the core elements and how these systems work.

الـ expert systems هما أنظمة كتخدم ب rules و facts باش تعطي حلول واضحة ف مجالات محددة. الخدمة ديالها منضبطة و مبنية على logic.

Core Concepts Explained

An expert system imitates how a domain expert thinks. It works with symbolic rules instead of statistical learning. The system checks conditions, applies rules, then produces a conclusion.

الـ expert system كيقلد طريقة التفكير ديال expert ف واحد المجال. كيستعمل قواعد منطقية و ماشي models تدريبية.

Main Components

1. Knowledge Base

The knowledge base stores rules and facts. Rules define decisions. Facts describe the domain.

2. Inference Engine

The inference engine reads rules. It checks conditions. It triggers matching rules. It moves step by step to final results.

3. User Interface

This interface lets the user enter data and read answers. It acts as the connection between the user and the system.

4. Explanation Module

This module shows which rules fired. It explains why the system reached the final result.

5. Knowledge Acquisition Module

This module helps experts add new rules without breaking the system. It builds the knowledge base safely.

ف الـ Darija:

Knowledge base كتجمع rules و facts.
Inference engine كيطلق القواعد و كيوصل للنتيجة.
Interface كيدخل user معلومات و كيعطيه الجواب.
Explanation كتوضح المسار.
Knowledge acquisition كتساعد باش نزادو قواعد جديدة.

How Expert Systems Work

The workflow stays simple.

User enters input
System checks rules
Inference engine selects matching rules
Engine triggers rules
System outputs the final answer

الطريقة: user كيدخل data، system كيقلب على rules لي كتنطبق، و النتيجة كتخرج مباشرة.

Inference Types

Forward Chaining

Starts from known facts and moves forward until reaching a conclusion.

Backward Chaining

Starts from a goal and searches for facts that support it.

ف الـ Darija:

Forward chaining كيبدا من facts.
Backward chaining كيبدا من goal.

Examples of Expert Systems

Medical diagnosis tools
Financial decision support
Fault detection systems
Legal reasoning tools

أمثلة: diagnosis، finance، fault detection.

Syntax or Model Structure Example

Below is a simple Python example that simulates a tiny rule-based expert system.

facts = {"fever": True, "cough": True}

rules = [
    {"if": ["fever", "cough"], "then": "flu"},
    {"if": ["fever"], "then": "possible infection"}
]

def infer(facts, rules):
    conclusions = []
    for rule in rules:
        if all(fact in facts and facts[fact] for fact in rule["if"]):
            conclusions.append(rule["then"])
    return conclusions

result = infer(facts, rules)
print(result)

هادا مثال بسيط كيبين كيفاش rules كتعطي conclusion بناءً على facts.

Strengths

Consistent decisions
Readable rules
Strong performance in narrow domains
Low cost after setup

Limitations

Hard to update
No learning ability
Weak performance outside the domain

Exercises

Define an expert system in one simple sentence.
List three components of an expert system.
Create a rule that detects a simple condition.
Write a small Python dictionary that represents facts.
Explain the difference between forward and backward chaining.
Add one new rule to the example code.
Build a mini knowledge base with five rules.
Explain why expert systems do not learn.
Describe one domain that benefits from expert systems.
Design a simple workflow for a rule-based medical assistant.

Conclusion

Expert systems remain useful for structured rule-based tasks. They follow logic, give stable answers, and offer readable reasoning. They form an important part of symbolic AI.

الـ expert systems مفيدين ف المهام لي فيها قواعد ثابتة. الخدمة ديالهم واضحة و النتايج مفهومة.

Ai With Darija