y4riss / expertsystem

expert system implementation using forward chaining

Computer Diagnostic Expert System

Introduction

This project implements a Computer Diagnostic Expert System, which assists users in diagnosing computer hardware issues based on observed symptoms. The system allows users to input symptoms and then generates a questionnaire for further precision in identifying the faulty hardware component. Once the questionnaire is completed, the system displays the faulty component, along with other potentially affected components.

Features

User Session

• Provides a user-friendly interface for accessing the application.

Allows users to:

• Input symptoms through an easy-to-use form.
• Manipulate entered facts (symptoms) if necessary.
• Deduces hardware issues based on entered symptoms.

Expert Session

• Offers an interface for experts to access the application.
• Recognizes experts via login credentials to allow manipulation of the rule base.

Allows experts to:

• Authenticate
• Add/update/delete a symptom
• Add/update/delete an issue (faulty component)

Security measures

• Authentication & Authorization using JWT (json web tokens)
• Protected routes
• Proteced apis using middlewares

Implementation Details

Knowledge Representation

The knowledge is represented using a rule-based system. Rules define relationships between symptoms and hardware issues.

Reasoning Type and Strategy

The system employs forward chaining reasoning to deduce hardware issues based on input symptoms. This strategy allows the system to determine the faulty component by matching input symptoms with predefined rules.

Technology Stack

• Frontend: React with TypeScript and Tailwind CSS for the user interface.
• Backend: Node.js with Express for server-side logic.
• Database: SQLite for storing user inputs and expert rules.

Usage

1. Clone the repository:

git clone https://github.com/y4riss/expertsystem.git
cd expertsystem

2. Install dependencies:

cd frontend && npm install
cd ../backend && npm install

3. Start the backend server:

cd backend
npm run dev

4. Start the frontend application

cd ../frontend
npm start

5. Access the application

| http://localhost:5173/

Admin page

Default creds

    username: admin
    password: admin

Login portal

| http://localhost:5173/login

Dashboard

| http://localhost:5173/dashboard

Api routes

• GET REQUEST

| http://localhost:3000/api/rules/issues

| http://localhost:3000/api/rules/symptoms

• POST REQUEST

| http://localhost:3000/api/auth/login

| http://localhost:3000/api/auth/logout

| http://localhost:3000/api/rules/issue

| http://localhost:3000/api/rules/symptom

| http://localhost:3000/api/diagnosis

• PUT / DELETE REQUEST

| http://localhost:3000/api/rules/issue/:id

| http://localhost:3000/api/rules/symptom/:id