Mechanic Miner 2023

Mechanic Miner 2023 is a co-creative game design tool for game developers that suggests game design ideas by exploring the source code of the game itself. It creates game mechanics from the game's source code using code reflection and evaluates these automatically using the state-of-the-art automated game-playing algorithm Go-Explore¹. Mechanic Miner 2023 can generate a diverse set of novel solutions for a simple 2D puzzle platformer within a time frame that suits iterative creative work.

This work is part of a master thesis paper titled Mechanic Miner 2023: Reflection-Driven Game Mechanic Discovery Powered by Go-Explore by Niels NTG Poldervaart, supervised by Rob Saunders and Michael Cook. Mechanic Miner 2023 (MM23) re-implements parts of the system described in a 2013 paper by Michael Cook, Simon Colton, Azalea Raad and Jeremy Gow titled Mechanic Miner: Reflection-Driven Game Mechanic Discovery and Level Design² (MM13) with a number of enhancements. Firstly, MM23 does not implement the procedural level generator which co-evolves levels with discovered game mechanics. Secondly, it’s implemented in Unity, a popular and robust game development environment, as opposed to Flixel. Thirdly, the number of supported data types and modifiers for TGMs has been expanded compared to MM13. Finally, the game-playing subsystem is driven by the state-of-the-art Go-Explore method.

Demonstration of MM23 running on a level that's impossible to solve the "normal" way (move left, move right, jump). With a population of 100 it explores the properties of various components associated with the player and level objects and applies modifiers to the values of these properties to create new game mechanics in an attempt to reach the level exit. In this image you can see examples where the agents use mechanics to invert or disable gravity or teleport across the pit.

Prerequisites

• Unity version 2023.1, which can be obtained with the Unity Hub (recommended) or the Unity downloads archive.

Usage

Project structure

The project contains to scenes:

• Assets/Scenes/MasterScene
  • Contains the MechanicMiner object, which controls the MM23 system.
    • The play mode "Run Evolution" runs the genetic algorithm with the current "Evolution Settings" for each level in the "Level Index List".
    • The play mode "Test Game Playing Agent" runs the automated game-playing agent in the first level of the "Level Index List" with a TGM defined in the "Set TGM manually, debug mode only" section.
    • The play mode "Manual Mode" lets you control the player in the first level of the "Level Index List" with a TGM defined in the "Set TGM manually, debug mode only" section.
• Assets/Scenes/SimulationScene
  • Contains the SimulationSceneController prefab, which has the SimulationInstanceController component. This component contains a list of levels the simulator can be run on. More levels can be added. It also has a slider that controls the physics time scale of the simulation. Be warned that setting this past the default value of 10f may make the simulation behave erratically.
    • Contains the player agent game object.
      • Has a list of components of the player object that can be used to construct a Toggleable Game Mechanic (TGM). This list can be modified, which changes to scope of the code domain MM23 can explore.
    • Contains GeneratedLevel, an empty level with a simple PCG level generator script. Not implemented in MM23.
    • Contains 9 hand-authored levels.
      • The root game object of each level has a list of components of the level that can be used to construct a Toggleable Game Mechanic (TGM). This list can be modified, which changes to scope of the code domain MM23 can explore.

Acknowledgements

• All sprite graphics are from the "1-Bit Platformer Pack", created and distributed by Kenney under a Creative Commons Zero license.
• Evolutionary algorithm is implemented using Genetic Sharp version 2.6.0 by Diego Giacomelli.

Footnotes

1. A. Ecoffet, J. Huizinga, J. Lehman, K. O. Stanley, and J. Clune, “First return, then explore,” Nature, vol. 590, no. 7847, p. 580–586, Feb. 2021. DOI: https://doi.org/10.1038/s41586-020-03157-9 ↩

2. M. Cook, S. Colton, A. Raad, and J. Gow, “Mechanic miner: Reflection-driven game mechanic discovery and level design,” in Applications of Evolutionary Computation, ser. EvoApplications 2013. Springer, 2013, pp. 284–293. DOI: https://doi.org/10.1007/978-3-642-37192-9_29 ↩