# Game Mechanic Alignment Theory and Discovery

arXiv: https://arxiv.org/abs/2102.10247 · HTML: https://mlsanity.com/paper/2102.10247/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 20 Feb 2021.

## Builds on (most cited first, 13 of 13)

- [General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms](https://mlsanity.com/paper/1802.10363/) (2018)
- [Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics](https://mlsanity.com/paper/1802.06881/) (2018)
- [Deep Reinforcement Learning for General Video Game AI](https://mlsanity.com/paper/1806.02448/) (2018)
- [General Video Game Rule Generation](https://mlsanity.com/paper/1906.05160/) (2019)
- [Mech-Elites: Illuminating the Mechanic Space of GVGAI](https://mlsanity.com/paper/2002.04733/) (2020)
- [Generating Levels That Teach Mechanics](https://mlsanity.com/paper/1807.06734/) (2018)
- [Intentional Computational Level Design](https://mlsanity.com/paper/1904.08972/) (2019)
- [Mario Level Generation From Mechanics Using Scene Stitching](https://mlsanity.com/paper/2002.02992/) (2020)
- [Deceptive Games](https://mlsanity.com/paper/1802.00048/) (2018)
- [AtDelfi: Automatically Designing Legible, Full Instructions For Games](https://mlsanity.com/paper/1807.04375/) (2018)
- [Automatic Mapping of NES Games with Mappy](https://mlsanity.com/paper/1707.03908/) (2017)
- ["Press Space to Fire": Automatic Video Game Tutorial Generation](https://mlsanity.com/paper/1805.11768/) (2018)
- [Deriving Quests from Open World Mechanics](https://mlsanity.com/paper/1705.00341/) (2017)

## Built on by (most cited first, 1 of 1)

- [TropeTwist: Trope-based Narrative Structure Generation](https://mlsanity.com/paper/2204.09672/) (2022)

## Abstract (arXiv)

We present a new concept called Game Mechanic Alignment theory as a way to organize game mechanics through the lens of systemic rewards and agential motivations. By disentangling player and systemic influences, mechanics may be better identified for use in an automated tutorial generation system, which could tailor tutorials for a particular playstyle or player. Within, we apply this theory to several well-known games to demonstrate how designers can benefit from it, we describe a methodology for how to estimate "mechanic alignment", and we apply this methodology on multiple games in the GVGAI framework. We discuss how effectively this estimation captures agential motivations and systemic rewards and how our theory could be used as an alternative way to find mechanics for tutorial generation.