When I posted the last post I posted, I was smugly certain that I’d be posting another post, (that very day!)all about how success had been achieved. It had been a while since I’d last looked at my figures. I had episode 200 to kick out the door, and some research to do for a new project. It wasn’t that long of a break, but just long enough to mean I had to read my journal to figure out where I was, and what I should do next. Once I knew what was what, I realized that what I had planned to do was pointless and useless, at least compared to the shiny new idea in my head.
Continue reading They’ve seen the light, they just need more practice.
I’m phrasing it as though they are looking at a light, because that’s what I’m doing—experimenting with implementing vision in an artificial life system.
There’s a factoid floating around that evolution cannot explain the eye. It’s based on a phrase written by Darwin. He had said that it is difficult to believe that something as complicated and well-constructed as the human eye could have come about by evolution. It was a rhetorical device. In the very next paragraph, Darwin lays out how evolution could produce an eye, or set of eyes, or whatever the given creature might happen to need.
Continue reading On vision and evolution
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will robots rule the world?
If we were to create an artificial intelligence that’s more intelligent than we are, would it take over and force us into extinction? Can a machine have a mind equal to or even better than our own? Today, we take a look at such questions, along with some side trips to sharks and whales and monkeys, and rocks and chocolate. I swear the candy thing really was relevant; I just got distracted.
Continue reading Ep 200: will robots rule the world?
So, here’s the game, implemented in game.java. A certain number of cycles is chosen, between a minimum amount, 10 at the moment, and a maximum, 50. Then a coin is flipped that determines whether the light is on or off for this round. One port will, when called, set b to 1 if the light is on, and -1 if it’s off. So far so good. This is enough for the figures to have the simplest eye imaginable. They can detect whether or not the light is on, something that even single cells are capable of. Next, we attempt to get them to react to whether or not the light is on, again, something that some of the simplest life forms can do.
Continue reading Damn local minima!
Their first task was easy. Can the digital organisms I wrote, called “figures,” learn to call a good command and avoid a bad one?
My first job was easy. It only took 6 lines of code. Each digital creature, each “figure,” has a certain amount of energy.
Figure.energy = 12,168;
Each time a figure has a turn, (when they execute one command,) the energy level drops.
When a figure is out of energy, it dies. It is completely deleted from the system, leaving behind it’s children, if it had any.
If (figure.energy < = 0)
Death.kill(figure) [scary music!]
Continue reading Their first task was easy
It’s about time I tried using my artificial life as an artificial intelligence. It’s been the goal since day one. Before I could do that, I had to come up with the algorithm in the first place, and then make sure that the figures could actually evolve. To evolve, they need to change, and some of those changes should help them survive and reproduce, passing on their successful tricks to their offspring.
Continue reading It’s about time I tried using my artificial life as an artificial intelligence
I’m sitting on the edge of a binary ocean, casting hyperdimensional nets into the infinite waters of possible programs. My digital creatures, which I call “figures,” will run on my computer for hours, until I finally catch something.
Continue reading It’s like fishing.
Written on Wednesday October3, 2018
Small sample sizes and all those caveats.
I’m tossing out the number of figures being born as a threshold. That will pop out of any of them at any time, and it’s just luck. One of them starts reproducing very quickly, and happens to find a window of relatively few mutation’s, and then skate on through to the finish line.
I retested the first five populations, snagged with a threshold of 100 and 100,000, if memory servs me. Looking at the most mutations given to a population, side by side results, first five and latest five.
Continue reading It bloody well works!
Written on Tuesday October 2, 2018
It seemed like a good idea. Let evolution solve the mutation problem for me. This is procrastination, as what I really need to do next is update the documentation and archive this version. It’s time to clean up the code and concentrate on making the system run faster. Still, I had a few days, and I’d notice that some populations were much more resistant to mutation than others.
Continue reading I was going to say that’s going nowhere, but looking at my notes just now.
Written on Thursday September 27, 2018
mutation each extinction
5.pop average 73
figures just fat
6.pop average 48
x.pop average 460 just fat longest with mu 131616 without only 38453
61.pop keeps doing too well to tell
I was trying to make s.pop into a six figures steady pop size stable population. I wanted to recreate 6.pop whose magic children have done so well that they climb off the measurement scale. I set things up so that s.pop was read from disc and stored in memory. Then, when s.pop died out, she’d be restored from memory rather than from disc. Mutations would happen unless the population was within a certain size range. Once it was all ready and as tested as I was willing to bother with, I let er rip.
Continue reading Think I was trying for the wrong thing.