Dazzlebug in Alaska

Some photos of the our citizen science game Dazzlebug being exhibited at the Anchorage Museum in Alaska as part of their Camouflage: In Plain Sight Exhibition running from the 28th October 2016 to the 5th Febuary 2017.

The bugs which have evolved throughout this exhibition and before have already been sent for processing by the researchers – more info on that here soon!

Dazzlebug in Alaska

Dazzlebug in Alaska Dazzlebug in Alaska Dazzlebug in Alaska

Crab camouflage citizen science game

The Natural History Museum London commissioned us to build a crab catching camouflage game with the Sensory Ecology Group at the University of Exeter (who we’ve worked with previously on the Nightjar games and Egglab). This citizen science game is running on a touchscreen as part of the Colour and Vision exhibition which is running through the summer. Read more about it here.





Artificially evolved camouflage

As the egglab camouflage experiment continues, here are some recent examples after 40 or so generations. If you want to take part in a newer experiment, we are currently seeing if a similar approach can evolving motion dazzle camouflage in Dazzle Bug.

Each population of eggs is being evolved against a lot of background images, so it’s interesting to see the different strategies in use – it seems like colour is one of the first things to match, often with some dazzle to break up the outline. Later as you can see in some of these examples, there is some quite accurate background matching happening.

It’s important to say that all of this is done entirely by the perception from tens of thousands of people playing the game – there is no analysis of the images at any point.











New camouflage pattern engine

One of the new projects we have at foam kernow is a ambitious new extension of the egglab player driven camouflage evolution game with Laura Kelley and Anna Hughes at Cambridge Uni.

As part of this we are expanding the patterns possible with the HTML5 canvas based pattern synthesiser to include geometric designs. Anna and Laura are interested in how camouflage has evolved to disrupt perception of movement so we need a similar citizen science game system as the eggs, but with different shapes that move at different speeds.

Here are some test mutations of un-evolved random starting genomes:




This is an example pattern program:


Robot nightjar eggshibition at the Poly, Falmouth

As part of this year’s Fascinate festival we took over the bar at Falmouth’s Poly with visualisations of the camouflage pattern evolution process from the egglab game.



This was a chance to do some detective work on the massive amount of genetic programming data we’ve amassed over the last few months, figure out ways to visualise it and create large prints of the egg pattern generation process. I selected family trees of eggs where mutations caused new features that made them difficult for people to spot, and thus resulted in large numbers of descendants. Then I printed examples of the eggs at different stages to see how they progressed through the generations.


We also ran the egglab game in the gallery on a touch screen which accidentally coincided with some great coverage in the Guardian and Popular Science, but the game kept running (most of the time) despite this.




The Poly (or Royal Cornwall Polytechnic Society) was really the perfect place for this exhibition, with its 175 year history of promoting scientists, engineers and artists and encouraging innovation by getting them together in different ways. Today this seems very modern (and would be given one of our grand titles like ‘cross-displinary’) but it’s quite something to see that in a lot of ways the separation between these areas is currently bigger than it ever has been, and all the more urgent because of this. The Poly has some good claims to fame, being the first place Alfred Nobel demonstrated nitro‚Äźglycerine in 1865! Here are some pages from the 1914 report, a feel for what was going on a century ago amongst other radical world changes:



News from egglab

9,000 players, 20,000 games played and 400,000 tested egg patterns later we have over 30 generations complete on most of our artificial egg populations. The overall average egg difficulty has risen from about 0.4 seconds at the start to 2.5 seconds.

Thank you to everyone who contributed their time to playing the game! We spawned 4 brand new populations last week, and we’ll continue running the game for a while yet.

In the meantime, I’ve started working on ways to visualise the 500Mb of pattern generating code that we’ve evolved so far – here are all the eggs for one of the 20 populations, each row is a generation of 127 eggs starting at the top and ordered in fitness score from left to right:


This tree is perhaps more useful. The ancestor egg at the top is the first generation and you can see how mutations happen and successful variants get selected.


Egglab – meet Ms Easter Robot Nightjar and her genetically programmed eggs!


We’ve released our latest citizen science camouflage game Egglab! I’ve been reporting on this for a while here so it’s great to have it released in time for Easter – we’ve had coverage in the Economist, which is helping us recruit egg hunters and 165,000 eggs have been tested so far over the last 3 days. At time of writing we’ve turned over 13 generations starting with random pattern programs and evolving them with small mutations, testing them 5 times with different players and picking the best 50% each time.

Here is an image of some of the first generation of eggs:


And this shows how they’ve developed 13 generations later with the help of many thousands of players:


We can also click on an individual egg and see how it’s evolved over time:


And we see how on average the time taken to find eggs is changing:


Technically this project involves distributed pattern generation on people’s browsers using HTML5 Canvas, making it scalable. Load balancing what is done on the server over three machines and a Facebook enabled subgame – which I’ll use another blog post to explain.

Egglab – pattern generation obsession

I’m putting the final pieces together for the release of the all new Project Nightjar game (due in the run up to Easter, of course!) and the automatic pattern generation has been a focus right up to this stage. The challenge I like most about citizen science is that along with all the ‘normal’ game design creative restrictions (is it fun? will it work on the browser?) you also have to satisfy the fairly whopping constraints of the science itself, determining which decisions impact on the observations you are making – and being sure that they will be robust to peer review in the context of publication – I never had to worry about that with PlayStation games :)



With this game, similar to the last two, we want to analyse people’s ability to recognise types of pattern in a background image. Crucially, this is a completely different perception process from recognition of a learned pattern (a ‘search image’), so we don’t want to be generating the same exact egg each time from the same description – we don’t want people to ‘learn’ them. This also makes sense in the natural context of course, in that an individual bird’s eggs will not be identical, due to there being many many additional non-deterministic processes happening that create the pattern.

The base images we are using are wrapped Perlin noise at different scales, and with different thresholds applied. These are then rotated and combined with each other and plain colours with the browser’s built in composite operations. Ideally we would generate the noise each time we need it with a different random seed to make them all unique, but this is way too slow for HTML5 Canvas to do (pixel processing in Javascript is still painful at this scale). To get around this we pre-render a set of variations of noise images, the genetic program picks one of four scales, and one of two thresholds (and one without threshold) and we randomly pick a new variation of this each time we render the egg. The image at the top shows the variation that happens across 6 example programs. Below are some of the noise images we’re using: