Luke Dicken and Heather Decker-Davis

@LukeD @HeatherMDecker

Lessons Learnt From Automated Content Generation in “Easy Money”?

Procedural Processes

About Robot Overlord Games

About "Easy Money?"

Easy Money? Demo

•Players expect lots of content in games •Players memorize static levels • Disinterested in replaying the same content

•PCG can keep it fresh. Imagine Temple Run with a fixed level layout

•Easier to store an algorithm than a set of assets

Why Generate Procedurally?

•An ample set of levels was needed, and PCG could help us achieve this • Mazes are generated semi-randomly • Items are deployed semi-randomly

• Today, we want to talk about how everything comes together and how PCG impacted our workflow and results

Procedural Generation in EM

Developing Procedurally

•Setting up conditions for a potential range of envisioned player experiences, rather than crafting highly specific settings

•Static vs. Procedural levels •Developers used to working strictly with

static content models may need to shift their mindset

•Designing interactions for a space that does not yet exist

•Spatial challenges and the realm of possible results • Be aware of potential unplayable situations

•Prototype your mechanics first •System-oriented or formulaic progression • as always, refine with testing

•Choose variables that follow your design goals and uphold a sense of fairness

Designing Procedurally

Designing Procedurally

•Test extremes of variables to find your target start and final level values

•Then build a formula to fill in between point A and point B

• Linear equations are a good starting point •Simple linear progression can be tweaked • For a distinct endgame feel, for example

Asset Creation

• Involve artists early so they can experiment with the specific PCG system

•Artists must think modularly and consider possible ways an asset may be positioned

•Several possible approaches • theme packages • color shifting • procedural texture

Asset Creation

•The art style in Easy Money? embraced the level structure while following the desired aesthetic

• Testing and prototyping art was helped in nailing down specifics methodology for asset construction

Prototyping

•Once general level characteristics were defined, 2D tiles were created

•This allowed us to refine both the generation system and the piece set • Easily iterate with little asset commitment

•Prototyping stages • 2D representation • 3D explorable levels • Adding the funds • Incorporating hazards

The Art Pipeline

• 2D tiles formed the blueprint for 3D pieces •Precise construction ensured each piece

would snap together properly on the grid •Pieces unwrapped, textured individually,

then tested in-game •To minimize draw calls, pack as much into

a single texture sheet as possible •Maximized reusability of pixel space for

shared components

Simple Maze Creation

•Creating mazes isn't overly hard • Just placing appropriate pieces into the

world where there is a “road to nowhere” •Very efficient approach

Algorithmic Maze Creation

Maze Verification

•We use some simple checks to validate the mazes produced are interesting • Number of dead-ends placed in the maze • Straight-line Distance from start to end • Number of pieces that have been placed

• If any of these checks fail, the maze is rejected and a new one generated

Post-hoc Verification vs Guided Generation

•Verification is a very simple process •Guiding generation is way more complex •Do we solve the simple problem multiple

times, or the complex problem once • Your mileage may vary

•Working out what the player is doing in your game is majorly important

•You need to pay attention and dig into the data to discover what’s actually going on

Analytics

Export and Replay of Content

•Analysis needs context •We need to have the ability to export

configurations of levels •Also need to be able to bypass PCG

system to load up a specific maze

Procedural Difficulty

•We have parameters that we're using: • for generation, for verification, for ingame

properties •So it isn't hard to see that if we start

manipulating these parameters we can start varying the difficulty

•As the game progresses, we can control the tone of the spaces being created

Player Modelling

•We can look at how the player is playing the game and what they are choosing to spend time on

•Then tailor the game to these tastes: • Players who prefer exploration can be given

larger spaces • Players who dash to the goal can be given tight

mazes with more hazards

Procedural Signposting

•How can we guide the player? •Signposting is how designers subtly

influence player perceptions • Lights under "important" doors • Cover position suggesting enemy locations

•Can we generate these signs on the fly? • In EM, we're playing with how we can use

our collectibles and hazards to help the player flow towards the goal (or misdirect)

Challenges

•Tools development may rack up a significant cost: both time and money

•Strong programming knowledge required • significant debugging and refining • responding to fringe cases

•Potential "sameness" generated spaces • may be combated through:

• peppering in unique content • combo approaches: both procedural and static

Challenges

•Balancing the power of your tools • Easy Money? started out with 100 levels

•PCG is not ideal for all types of games

Summary

•Must evaluate the pros and cons of PCG on a project-specific basic

•The methods here can be leveraged for many different styles of games and tailored to many different level structures

Takeaways

•Break levels down into modular pieces •Establish the PCG system early • Tight integration between artists, designers

and developers • PCG will affect decisions made by all groups

•You can use PCG to turn your games to 11!

Alpha - http://easymoney.robotoverlord.co.uk/

Robot Overlord Games

Heather Decker-Davis !heather@robotoverlord.co.uk

@heathermdecker

Luke Dicken !luke@robotoverlord.co.uk

@LukeD