import React from 'react'; import { BookOpen, Users, Code, FileText, GraduationCap, Calendar } from 'lucide-react'; export const GroupInfo: React.FC = () => { return (
{/* Paper-style header */}

Artificial Intelligence Project

Package Delivery Search Agent:
A Comparative Study of Informed and Uninformed Search Algorithms

Academic Year 2025-2026
{/* Authors section */}

Team Members

{[ { name: 'Kacem Mathlouthi', role: 'GL 4/2' }, { name: 'Mohamed Amine Houas', role: 'GL 4/1' }, { name: 'Oussema Kraiem', role: 'GL 4/2' }, { name: 'Alaeddine El Zaouali', role: 'GL 4/2' }, ].map((author) => (

{author.name}

{author.role}

))}
{/* Abstract */}

Abstract

This project presents an interactive visualization tool for comparing search algorithms in the context of package delivery optimization. We implement and analyze six fundamental search strategies: Breadth-First Search (BFS), Depth-First Search (DFS), Iterative Deepening Search (IDS), Uniform Cost Search (UCS), Greedy Best-First Search, and A* Search. The system models a grid-based city environment with varying traffic conditions, blocked roads, and tunnel shortcuts, providing a comprehensive testbed for evaluating algorithm performance in terms of path optimality, computational efficiency, and memory utilization.

{/* Project Details */}

Technical Implementation

Backend

Python 3.11 + FastAPI

Frontend

React 19 + TypeScript + Vite

State Management

Zustand

Visualization

SVG + ShadCN

Styling

Tailwind CSS v4

Communication

REST API

{/* Algorithms */}

Implemented Algorithms

{[ { name: 'Breadth-First Search', code: 'BFS', type: 'Uninformed', optimal: 'Yes*', complete: 'Yes' }, { name: 'Depth-First Search', code: 'DFS', type: 'Uninformed', optimal: 'No', complete: 'No' }, { name: 'Iterative Deepening', code: 'IDS', type: 'Uninformed', optimal: 'Yes*', complete: 'Yes' }, { name: 'Uniform Cost Search', code: 'UCS', type: 'Uninformed', optimal: 'Yes', complete: 'Yes' }, { name: 'Greedy Best-First', code: 'GBFS', type: 'Informed', optimal: 'No', complete: 'No' }, { name: 'A* Search', code: 'A*', type: 'Informed', optimal: 'Yes', complete: 'Yes' }, ].map((algo) => ( ))}
Algorithm Type Optimal Complete
{algo.name} ({algo.code}) {algo.type} {algo.optimal} {algo.complete}

* When step costs are uniform

{/* Heuristics */}

Heuristic Functions

Manhattan Distance

h(n) = |xn - xg| + |yn - yg|

Euclidean Distance

h(n) = sqrt((xn - xg)2 + (yn - yg)2)

Tunnel-Aware Heuristic

Considers available tunnel shortcuts to improve estimate accuracy

{/* Footer */}

This project was developed as part of the Artificial Intelligence curriculum

); }; export default GroupInfo;