Flocking System Painting with Pixels

Flocking painting Live Video 8 Aug 2017

This is one Simon’s most beautiful projects recently! Simon saw the idea to link the webcam image to the boids of a flocking system in a video by Daniel Shiffman, but the code featured in this project Simon wrote himself. The Flocking code is based on Daniel Shiffman’s example from his book The Nature of Code. (Flocking is a steering behavior that consists of separation, alignment and cohesion – which are also steering behaviors – combined).

Simon’s sis also posed for the camera:

Flocking painting Live Video 8 Aug 2017 2

Simon is also about to make a JavaScript version of this fun project, because JavaScript allows to host it easily online, so that everyone can play with it. With JavaScript, he may even be able to write it in an online editor, so there won’t even be a need to host it. Check in later for an update to this post!

UPDATE: Simon encountered a problem trying to translate his Flocking System Painting with Pixels into JavaScript: P5 runs much slower than Processing after Simon added steering behavior. He doesn’t know how to solve this. Simon’s JavaScript code is online at https://codepen.io/simontiger/pen/ZJKBbN?editors=0011

Evolutionary Steering Behaviors Game

Note: See the update at the bottom of this post!

We’ve had quite a dramatic situation here for the past couple of days, after Simon turned Daniel Shiffman’s Evolutionary Steering Behaviors Coding Challenge into a game in Processing (Java) and then also in JavaScript (with p5). After completing the game in JavaScript, Simon wanted to add a new feature – a checkbox he programmed using the p5.js library. The checkbox would give the player the option to play with or without the timer, adjust the timer and also had a “New game” button. In the end it turned out that the checkbox didn’t really work. Simon was very upset and it took me hours to talk him into putting the game online even though the checkbox didn’t function (he wanted everything to be perfect) and ask for advice. “I have got a problem with a p5 element: In my setup function, I defined my checkbox. In my reset function, my checkbox is undefined. Why?” – Simon asked in the “Share Work” section of the Coding Train Slack channel, where he has the opportunity to communicate with experienced programmers. He received quite a lot of help and was enthusiastic about it at first, but for some reason, he hasn’t tried the solutions he was suggested. Perhaps it’s his gut feeling that the bind function suggested is still too difficult at the moment. I have decided not to push anymore and trust him on this one, although it’s always a dilemma for me whether I should sometimes “force” him into taking instructions from others or let him solely rely on his fantastic intrinsic autodidact mechanisms. The second seems to work better in terms of the learning process, but I do push him into sharing his work.

Evolutionary Steering Behaviors game. Asking help in Slack 10 Jul 2017 3Evolutionary Steering Behaviors game. Asking help in Slack 10 Jul 2017 4Evolutionary Steering Behaviors game. Asking help in Slack 10 Jul 2017 5Evolutionary Steering Behaviors game. Asking help in Slack 10 Jul 2017 6Evolutionary Steering Behaviors game. Asking help in Slack 10 Jul 2017 7Evolutionary Steering Behaviors game. Asking help in Slack 10 Jul 2017 8

Evolutionary Steering Behaviors game. Asking help in Slack 10 Jul 2017 2

Simon’s game is online at: https://simon-tiger.github.io/Game_SteeringBehaviorsEvolution/SteeringBehaviours_EvolutionGame_p5/

In the videos below Simon shows how he made the game. It’s an ecosystem type of genetic algorithm (with no generations), where the organisms (autonomous steering agents) clone themselves. The autonomous steering agents evolve the behavior of eating food (green dots) and avoiding poison (red dots). Simon added two invaders into the game, one giving food and the other randomly spreading poison. The player can control the “good” invader by moving him and making new food. The goal of the game is to make the agents survive for as long as possible.

The Processing (Java) version:

The thinking behind the game (Simon explains everything at the whiteboard):

The JavaScript version (now online):

In the last video, Simon talks about his problem with the p5 element.

 

Evolutionary Steering Behaviors game seek algorithm part 1. DESIRED equals TARGET minus POSITION:

Evolutionary Steering Behaviors game seek algorithm part 1. DESIRED equals TARGET minus POSITION 4 Jul 2017

Evolutionary Steering Behaviors game seek algorithm part 2. STEERING equals DESIRED minus VELOCITY:

Evolutionary Steering Behaviors game seek algorithm part 2. STEERING equals DESIRED minus VELOCITY 10 Jul 2017

UPDATE: When Simon saw Daniel Shiffman’s comment on Slack this morning (Daniel saying Simon did a fantastic job and that he might even include Simon’s game in the next Live Stream), he sat down and applied the bind function as suggested by his older peers above – without any incentive on my behalf! And it worked! I think we’ve hit a true milestone again. Simon has this growing feeling that he’s got friends out there, his tribe, who understand and who are ready to help.

One day later: Simon had another chat with his friends on Slack and got a lot of help with the last remaining small bug in his game (the New Game button didn’t start a new game if the player had chosen to play with no timer but jumped to Game Over instead). In the video below, Simon shows how that problem got solved:

Autonomous Agents. Steering Behaviors. Flocking.

Simon’s full-screen take on Flocking, based on Chapter 6 from The Nature of Code (Autonomous Agents). The code comes from Daniel Shiffman’s tutorial Combining Behaviors: Flocking.

In Simon’s words, Autonomous Agents need to have action/ selection, steering (for example, for Flocking you need Separation, Alignment, Cohesion programmed using the neighbours, desired and current velocity) and locomotion (position, velocity, acceleration, “own vector stuff” or a physics library).

 

 

Simulating Evolution: Evolutionary Steering Behaviors

 

On Wednesday Simon went on with playing god (evolution simulation) and translated Daniel Shiffman’s Evolutionary Steering Behaviors Coding Challenge from JavaScript to Java.  The goal of the challenge is to create a system where autonomous steering agents (smart rockets) evolve the behavior of eating food (green dots) and avoiding poison (red dots).

This challenge is part of the spring 2017 “Intelligence and Learning” course at NYU’s Tisch School of the Arts Interactive Telecommunications Program. Simon was especially happy to find out that Daniel Shiffman left a couple of personal comments praising Simon’s progress and offering help in pushing his code to Danniel’s GitHub repo.

Here is Simon’s translation on GitHub: https://github.com/simon-tiger/steering-behaviors-evolution

The rockets have their own DNA consisting of four genes:

IMG_4531

The challenge step by step:

 

 

 

 

 

Autonomous Agents and Genetic Algorithms

Today Simon spent hours watching videos and reading Daniel Shiffman’s book The Nature of Code, concentrating on more complicated matters than ever: autonomous agents and evolution (genetic algorithms). In The Nature of Code there are two separate chapters covering these topics.  The term autonomous agent is defined as “an entity that makes its own choices about how to act in its environment without any influence from a leader or global plan”. This basically means that Simon has decided to try programming smart entities, that are like living things (have their DNA’s, behaviours, evolution).

The Autonomous Agent chapter also talks about steering force:

 

In the videos below, Simon has studied the steering behaviors in Daniel Shiffman’s code (Seek, Flee and Arrival) and changed the code slightly (third video) to be able to switch between the behaviours with a click of the mouse:

I also saw Simon go through the paper called Steering behaviours For Autonomous Characters by Craig W. Reynolds and reading about Persuit and Evade steering behaviours at this game development page.

In the video below Simon adjusted the Evolve Flow Field code to be able to see the possible behaviours (velocity vectors) of his smart rockets.

The code comes from the chapter Evolution of Code in Daniel Shiffman’s book, the chapter mainly devoted to genetic algorithms: it looks at the core principles behind Darwinian evolutionary theory and develops a set of algorithms inspired by these principles.

One of the most interesting notions Simon came across today was fitness, as in survival of the fittest. In the video you see Simon creating obstacles for the smart rockets. Together we observed how, as many generations of rockets passed, they learned to go around the obstacles better. This was possible because the rocket’s fitness was programmed to be greatly reduced every time it hit an obstacle.

All the rockets also have DNA’s: “We are marching through the array of PVectors and applying them one at a time as a force to the rocket”, Daniel Shiffman explains.

Simon also learned about genotypes and phenotypes, mating pool, crossover and mutation. He loved Daniel Shiffman’s example about haw many generations of strings with 18 random “genes” it would take to write “To be or not to be”.

Shakespear Evolution 18 Apr 2017