Smart Food

Overview

Developed for the Knowledge-Based Systems course, SmartFood / FoodExpress is a two-part AI and optimization project designed to handle dish recommendations and delivery route optimization.

The project was split into two core components:

• Part A (SmartFood): A knowledge-based recommendation engine built with Prolog and Perl that analyzes user tastes through a quiz interface to suggest tailored food options.
• Part B (FoodExpress): A route optimization engine that calculates the most efficient delivery paths for drivers to maximize profit, minimize transit time, or optimize custom delivery metrics.

Technical Innovation & Architecture

While traditional implementations for this course relied on basic shell runtimes and desktop UIs (Java Swing), this project introduced a modern full-stack web architecture by embedding the Prolog inference engine inside a web server.

• SWI-Prolog & Java Bridge: Integrated the SWI-Prolog API into a Java Spring Boot backend, wrapping rule-based logic inside RESTful endpoints.
• Decoupled Web Frontend: Built a responsive web frontend using Node.js, HTML, CSS, and JavaScript, replacing legacy desktop UI frameworks with a web interface.
• Route Optimization Algorithms: Implemented pathfinding and decision-making logic inside Prolog to solve complex routing constraints for delivery fleets.

Challenges & Key Learnings

Single-handedly executing this project provided direct exposure to logic programming, system integration, and early machine learning concepts:

• Logic Programming Foundations: Mastered declarative programming paradigms in Prolog and text-processing/scripting in Perl.
• Cross-Language Integration: Successfully bridged SWI-Prolog’s native library runtime with Java, handling memory and state management between Java objects and Prolog predicates.
• Full-Stack Adaptability: Transformed a traditional desktop assignment into a modern REST-driven web app, earning a perfect score (20/20).