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Learning Autonomic Robots
Daath writes "The 27th of March, Professor Noel Sharkey et al starts a colony of living robots. 15 predators and 6 prey. It's an experiment in artificial evolution out of the Creative Robotics Unit at Magna. Here's a quote: 'The Living Robots have one goal, to obtain enough energy to survive and breed. The prey find their food from light sensors within the arena, while the predators feed off prey by stalking and chasing them before sucking away their power.'
Magna has two articles, 'Predator and Prey Robots set up home at Magna' and 'Ground breaking Robotics experiment previewed'. "
Wouldn't you need more prey than predators to obtain a viable population? This would be much cooler as well if both predators and prey could mate with their own species, i.e exchange randomization factors for their strategies. Then the best would survive, and the dead (drained) could be recycled as offspring.
so basically this is just an incredibly remedial version of DaisyWorld. No news here.
The article in this month's IEEE Spectrum magazine, experimentation with modular robotics, seems more worthy of the label "ground-breaking."
Tierra was by Tom Ray, a pioneer in the AL field. It was a great idea, but failed to turn around with interesting biodiversity. You'd create creatures, they'd optimize themselves, some variants and parasites would evolve, but then things would simmer down within a few hours and you'd be in a steady state for ever.
t ml, but it too failed to spark that je ne sais quois, that spark of life.
:)
Network Tierra was Ray's response to this. It was supposed to allow a "Cambrian explosion" of biodiversity, by providing tons of (networked computer) space for the little creatures to explode into, and then specialize, in. This led to interesting migration behavior, and one of my all-time favorite web-pages http://www.isd.atr.co.jp/~ray/pubs/images/index.h
Anyways, it did spark Avida and the Digital Life Lab at Cal Tech. Avida is essentially a deeper look at the fundamentals behind AL. In Tierra, I think the design philosophy was something like "make it look a lot like a living ecological system and the life-force will appear out of the ether", and actually, Tierra was a great leap forward beyond more mundane genetic programming a la John Koza.
Avida, on the other hand, is much more systematic in exploring the parameter space (which is large and sensitive) for setting up an AL system. This turned out to be fruitful, as Adami found that only when certain, very narrow, environmental conditions were met would the little creatures start outsmarting that Creationist boogeyman, the Second Law of Thermodynamics.
Turns out that Tierra didn't have spatiality (needed to be more restrictive on who could sleep with who) and mutation rates (some power law math that's way over my head) set right.
But the real punch-line to this whole story is that the direct beneficiary of these insights in Microsoft! Hah!
Microsoft was funding Adami's work because Windoze crashed too much. They were searching for a way of programming, in this case using closed instruction sets like Avida's (another deep topic), that would be inherently robust to problems like seg faults and illegal instructions.... e.g. Adami's instruction set was engineered so that little programs (creatures) couldn't crash the Avida VM when they mutated into new, unknown programs.. or in Windoze's case, when a coder did something stoopid. It's funny that MS was researching this, since releatively low-tech solutions such as protected memory and QA take care of this. (not to mention Java
freality.com
p.s. Since when do research experiments post crowd-pleasing previews? That's for Hollywood.
Another roboticist, Mark Tilden(http://www.wired.com/wired/archive/2.09/til den.ht), actually builds robots that have no CPU's. He fashions them after insects by having just a simple circuit board, after an action proves unsuccessful it gets changed slightly and like this the robots learns. I heard about one experiment where he took a number of solar powered robots (built out of things like walki-talki parts) that were programmed (I'm not sure of all the details) to find light and set them in a room with a few light sources. He observed behavior that some of the larger ones broke smaller robots and ended up using their parts to form a barrier around the light source.
I stole this Sig
>
I can't see his site, but it may be the case that he's not trying to mimic nature. What you describe above is very conventional in the field of genetic algorithms, and it works very well for many types of problems; it's inspired by biological evolution, but it's not a model of biological evolution.
Back to a couple of your specific comments:
> Just looking at survival rate is a measure for measuring fitness, but it's too crude a method for improving ones genes.
No, it works quite well for very many problems. You should be able to find a simulator you can download from the internet to demonstrate this.
> Besides that now every surviving bot has the same amount of fitness (offspring).
For genetic algorithms, 'fitness' is rarely measured by the number of offspring. For evolving agents it is usually measured by the score at performing some task, or sometimes by bare survival in some environment. And letting them all have the same amount of children is no problem, because it maintains some diversity in the genome.
Sometimes experimenters do let the highest scorers make more babies, but that is not necessary to a GA. I usually keep the best 10% of the population (or 50%, if resource limitations make me use a small population), and I let each of the keepers make an equal amount of babies with randomly selected partners until the population is filled out again. This works, in practice.
[And thank you oh-so-much for bringing this topic up, because while writing the paragraph above I think a bug in my latest simulator occured to me!]
Sheesh, evil *and* a jerk. -- Jade
choose a mate with the most similar genes.
... help the body recognize ... an invader such as a bacteria or virus. ... Different MHC molecules are good at recognizing different invaders. By a choosing a mate whose MHC molecules are different, the female mouse is ensuring that her offspring will have a wide variety of MHC molecules that which can identify a large array of invaders and thus promote survival.
... Women were then presented with six shirts - three from men with similar MHC genes, and three from men with different MHC genes from their own. The results showed that the women preferred the scents of men whose MHC genes were different from their own. The scent of men with similar MHC genes often remind the women of a relative's odor, such as a brother or father while the smells of MHC dissimilar men would often remind them of a past or current boyfriend. This suggests that body odor might have influenced past and current decisions on who to date.
Actually there are signifigant pressures to select a mate with different genes.
MHC stands for major histocompatibility complex. These genes
Research done on human females shows that they too prefer men whose MHC genes are the least similar to their own. In an experiment, men were given an unscented T-shirt and were asked to wear it for two nights in a row.
In many species members of one sex stay with their group their entire lives, but the other sex leaves to find a different group upon reaching sexual maturity.
In humans "exotic" is usually equated with "attractive".
But, like pretty much anything in biology, there's a mixed bag of often contradictory effects.
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