Matthew (He/Him) is a Writer, Programmer, Teacher, and Game Designer based in Melbourne.

He completed his undergraduate degree in Computer Science at Swinburne University of Technology in 2021. Matthew previously worked at Firemonkeys as a Game Designer, working on Need For Speed: No Limits. He is now one-half of the indie game studio Toot Games (funded by VicScreen).

Matthew has worked with the City of Melbourne to produce Design Your Own Indie Game - a series of free workshops teaching beginners the fundamentals of hobby game development. In 2024 Matthew completed a games commision contract making an educational game for Swinburne’s Journalism & PR degrees. In 2022 he completed a residency at The Arcade Melbourne, working independently as a part of their South Melbourne Residency Tenure program.

Matthew respectfully acknowledges that he works, makes, and learns on land that belongs to the Wurundjeri People of the Kulin Nation. Sovereignty was never ceded. Always was, always will be, Aboriginal land.

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AI Programming

Jadex DCOP Solver

Over four months in 2020, I worked in a small trio of undergraduates to create a program that uses an existing solving algorithm (Adopt) to solve Distributed Constraint Optimisation Problems (DCOP). The program used Active Components (also known as JadeX) - a framework for programming distributed concurrent systems and was modeled off of the popular Python library, PyDCOP. Below is a snippet of the console output of communication messages being sent over the multi-agent framework to begin solving the DCOP. The report linked below explains the project in greater detail.

Github Repo
Project Report

Pictured below was a very gratifying output to read in the console, telling the team that our distributed network of agents were cooperating. 

Boids & Bees Project

To learn the core principles of Games AI I tinkered around with some simple boids, and expanded on that code to create a several small projects.
    The first was a classic flocking simulation. Layering forces of coupling, cohesion, and allignment in groups of agents so that they move around in flocks.
    The next is a RTS style pathfinding simulation where two “beehives” deploy pathfinding bees and hornets to collect pollen and fight over flowers. The simulation used A* searches to generate paths.
    The last is a trickshot simulation where an agent uses depth limited searches to find the quickest way to shoot all the other agents in a scene with the fewest shots fired. resulting in a bunch of two for one shots.

Bees Code Report
Bees Data Analysis
Trick Shots Code Report

These three proof-of-concept builds were made in Python using the Pyglet library.