It was a java applet (sigh) and unfortunately I have not been able to find a working version. That version based on his three "steering" mechanisms had very realistic movement. Other versions, including this one, which are good do not have that same kind of quality. They look like simulations whereas the Reynolds version, for whatever reason, seemed much closer to watching an actual flock.
No criticism intended, it would just be nice to understand the why the difference.
Looking briefly at the code, it seems the fitness function is simply how close the boids are?
I don't know if this will address your question, but I had a long-standing question about boids that might overlap, which I coincidentally only resolved to my satisfaction a month ago. Here's the Lua code I ended up with:
function update_positions(boids, dt)
local max_speed = 0.5 -- per frame
local max_accel = 20 -- per second
local max_turn_angle = math.pi/6 -- per second
for _,boid in ipairs(boids) do
boid.pos = vadd(boid.pos, boid.velocity)
end
for i,boid in ipairs(boids) do
local accel = {x=0, y=0}
accel = vadd(accel, vscale(avoid_others(boid, boids), 20*dt))
accel = vadd(accel, vscale(seek_others(boid, boids), 10*dt))
accel = vadd(accel, vscale(align_with_others(boid, boids), 10*dt))
accel = vadd(accel, vscale(remain_within_viewport(boid), 40*dt))
local curr_heading = vnorm(boid.velocity) -- could be nil
accel = vclamp2(accel, max_accel*dt, curr_heading, max_turn_angle*dt)
boid.velocity = vadd(boid.velocity, accel)
boid.velocity = vclamp(boid.velocity, max_speed)
end
end
Here, avoid_others, seek_others and align_with_others are the 3 rules you can find on Wikipedia (https://en.wikipedia.org/wiki/Boids): separation, cohesion, alignment. Each of the functions returns a unit vector, which I then weight using vscale.
The key is the last 4 lines. My intuition here is that the way muscle mechanics work, there are limits on both how fast you can accelerate and also how much you can turn per unit time. That's what vclamp2 is doing. It separately clamps both magnitude and angle of acceleration.
My rough sense after this experience was:
* Boids is not a simple program the way the Game of Life or Mandelbrot set is. The original paper had tons of nuance that we gloss over in the internet era.
* Every implementation I've found is either extremely sensitive to weights or does weird stuff in the steering. Stuff like subtracting velocity from acceleration when the units are different, and so on. There may be a numeric basis for them, but it's never been explained to my satisfaction. Whereas my vclamp2 idea roughly hangs together for me. And the evidence it's on the right track is that a wide variety of weights (the 10s, 20s and 40s above) result in behavior that looks right to me.
Wow! Thanks. The thought about "Boids is not a simple ..." is new to me and very good. The other vector in this is the evolution/genetic algorithm idea. It raises the question of what are the benefits of flocking? And could you plug those into a genetic algorithm to test survival.
It seems like perhaps the visual inputs are another interesting area. What do I (as a boid) do when I see one boid in front of me go right, one go left, for example.
But thanks!!
Yeah, the original paper gets into some of that. It was about 3D flocking! And Reynolds was very much thinking about what each bird sees, the minimum angle to turn to avoid a collision, etc. All on a then-powerful graphical workstation.
Just a clarifying note, Craig Reynolds is the original researcher for Boids, and he did have a Java applet implementation in the above page. But the original Boids simulation was from 1986, almost a decade prior to Java applets.
The original paper, published in 1987, is "Flocks, herds and schools: A distributed behavioral model"[1]. The implementation was done in Lisp on a Symbolics 3600 Lisp Machine.
Edit: One quite interesting paragraph from the paper regarding performance:
The boid software has not been optimized for speed. But
this report would be incomplete without a rough estimate of the actual performance of the system. With a flock of 80 boids, using the naive O(N²) algorithm (and so 6400 individual boid-to-boid comparisons), on a single Lisp Machine without any special hardware accelerators, the simulation ran for about 95 seconds per frame. A ten-second (300 frame) motion test took about eight hours of real time to produce.
Once again, amazing how far hardware has advanced.
I've had a lot of fun playing with BBC Microbot (https://bbcmic.ro/). If you add &experimental=true to the URL it will add a rocket ship button underneath the display. Clicking it sends the code off to beebjit and runs it for 10,000 seconds instantly, allowing you to do unreasonable things such as this: https://bbcmic.ro/?t=bC9Go (not mine)
oh, so i wasn't really aware that there was a original boid sim (I will check it today). mostly I saw it on some other demos and I wanted to add this behaviour of signaling boids which are far away + color code based on genome + do a simple cross-mutate. and yes you are right about fitness func.
>Early motion tests of the boids model of flocking and related collective motion (herds, schools, crowds). Recorded on a Symbolics Lisp Machine between 1986 and 1987.
Stanley and Stella in “BREAKING THE ICE” - Original Symbolics Tapes Restored & Remastered, 1080p:
>Pioneer Craig Reynolds shares his groundbreaking work in artificial life and computer animation. Reynolds describes his lifelong fascination with simulating natural complexity computationally. By modeling flocking birds from the bottom up, based on simple rules for how individual birds move, Reynolds made the astonishing flock emerge. This agent-based approach allowed new insights into collective behavior impossible to gather from nature alone. Reynolds reflects on the significance of his iconic 1987 "Boids" model for animating birds in films like Batman Returns. But its influence extends further - to complexity science and understanding real-world systems like traffic flow and crowd dynamics. According to Reynolds, the beauty of simple models is their emergent properties. By distilling phenomena to basic rules, computer simulation uncovers new truths about the world. Join this fascinating conversation with a legend of artificial life and computer graphics.
https://www.red3d.com/cwr/boids/
It was a java applet (sigh) and unfortunately I have not been able to find a working version. That version based on his three "steering" mechanisms had very realistic movement. Other versions, including this one, which are good do not have that same kind of quality. They look like simulations whereas the Reynolds version, for whatever reason, seemed much closer to watching an actual flock.
No criticism intended, it would just be nice to understand the why the difference.
Looking briefly at the code, it seems the fitness function is simply how close the boids are?
Very cool!