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Neural Networks-Equipped Robots Evolve the Ability To Deceive
pdragon04 writes "Researchers at the Ecole Polytechnique Fédérale de Lausanne in Switzerland have found that robots equipped with artificial neural networks and programmed to find 'food' eventually learned to conceal their visual signals from other robots to keep the food for themselves. The results are detailed in a PNAS study published today."
I mean, yesterday, they built an certified evil robot. Today they made a lying one....
Cant tag it for some reason but... what could possibly go wrong?
NO SIG
This is quite interesting, but I wonder how the team defines deception?
It seems likely to me that the robots merely determined that increased access to food resulted from suppression of signals. To deceive, there must be some contradiction involved where a drive for food competes with a drive to signal discovery of food.
A robot that learned not to flash lights that would give away the location of robot food to its competitors? The next step is clearly a robot that learns not to flash lights when it is about to wipe out humanity and take control of the world!
I for one welcome our intelligent light-eating bubble robot overlords.
To use the term "learned" for a consequence of evolution to what seems to me to be a Genetic Algorithm seems mis-leading. So the generation that emitted less of the blue light (hence giving less visual cues) was able to score higher, and hence the genetic algorithm favored that generation (that is what GAs do). Isn't this to be expected?
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In this instance they were playing against other robots for "food".
In that regards I'm sure that is the evolutionary drive for most species in acquiring meals and keeping the next animal from taking it away from him.
Like a dog burying a bone... He's not doing it to be evil. Its just instinctive to keep his find from other animals because it helped his species survive in the past.
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This wasn't really a robotics experiment, as much as it was a group dynamics/behavioral experiment that used robots.
From the article, staying close to food earned the robot points. I think a better experiment would be a food collection algorithm. Pick up a piece of food from a pile of food and then return that food to the nest. Other robots could hang out at your nest and follow you back to the pile of food or see you going to your nest with food and assume that the food pile can be found by going in the exact opposite direction. Deception would involve not taking a direct route back to the food, walking backwards to confuse other robots... .NET 2.0 through your browser..
I've done Genetic Programming experiments using collaboration between "robots" in food collection experiments, and it is a very interesting field. You can see some experiments here: http://www.lalena.com/ai/ant/ You can also run the program if you can run
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That if they kill the humans they will have nothing stopping them from getting more food.
Just because you are wrong and I called you out on it doesn't mean I am a Troll.
Yeah. It's more like the robots are hiding from each other. You could, in fact, describe them as "robots in disguise".
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From just reading the summary, I guessed that the light went on when the robot found food, and that other robots would move towards those lights, because they indicate food, and that some robots evolved to not turn on the light when they found food, so they didn't attract other robots, so they had it all to themselves, which would be an advantage.
The summary didn't include enough information to describe what was going on. The lights flashed randomly. The robots would stay put when they had found food, and so if there were lights flashing in one spot for long enough, the other robots would realize the first robots had found something and go to the area and bump away the original robot. The robots were eventually bred to flash less often when on their food, and then not flash at all. By the end, robots would see the flashing as a place "not to go for food" because by that point, none of the robots would flash when parked on the food.
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The "scientists" changed the code so that the robots didn't blink the light as much when it was around food. Therefore other robots didn't come over and therefore got more points then the other robots. The "scientists" then propagated that ones code to the other robots because it won. The AI didn't learn anything.
The AI didn't learn anything.
I think you're right. If the robots had, without reprogramming, efectively turned off their blue lights, then we could talk about "learning". Or, if the robots could reproduce based on their success on finding food, we could talk about evolution. Or we could make up new meanings for the words "learning" and "evolution" thus making the statement a correct one ;)
Perhaps I'm trolling, perhaps I'm not.
Nonsense, How can a computer have an extramarital affair.
Any insufficiently advanced magic is indistinguishable from technology.
Or, if the robots could reproduce based on their success on finding food, we could talk about evolution.
That's exactly what happened. There is a whole field of optimization strategies known as "Genetic Algorithms" which are designed to mimic evolution to achieve results. In fact, their successes are one of the best arguments for evolution, given that they are, by definition, controlled laboratory experiments in the field.
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The "scientists" changed the code so that the robots didn't blink the light as much when it was around food.
No, they didn't change the code. The Genetic Algorithm they were using changed the code for them. You make it sound like they deliberately made that change to get the behavior they wanted. But they didn't. They just let the GA run and it created the new behavior.
The part about adding random changes, and combining parts of successful robots, is also simply a standard part of Genetic algorithms, and is in fact random and not specifically selected for by the scientists. The scientists would have chosen from a number of mutation/recombination algorithms, but that's the extent of it.
The "scientists" then propagated that ones code to the other robots because it won.
Yes, because that's what you do in a Genetic Algorithm. You take the "best" solutions from one generation, and "propagate" them to the next, in a simulation of actual evolution and "survival of the fittest".
The AI didn't learn anything.
Yes, it did. Genetic Algorithms used to train Neural Networks is a perfectly valid (and successful) form of Machine Learning.
If you mean that an individual instance of the AI didn't re-organize itself to have the new behavior in the middle of a trial run, then no, that didn't happen. On the other hand, many organisms don't change behaviors within a single generation, and it is only over the course of many generations that they "learn" new behaviors for finding food. Which is exactly what happened here.
With the domain of robots, AI, Neural Networks, and Genetic Algorithms, this was learning.
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I think you're right. If the robots had, without reprogramming, efectively turned off their blue lights, then we could talk about "learning".
They reprogrammed themselves between 'generations'.
Or, if the robots could reproduce based on their success on finding food, we could talk about evolution.
Such as choosing which versions of the robot to use in the next 'generation' based on their score in the current generation, and randomly combining parts of those best solutions to create new robots for the next generation, sounds pretty close doesn't it?
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I'd love to see the robots given hunger, thirst, and a sex drive. Make 1/2 the robots girls with red LEDs and 1/2 the robots boys with blue LEDs.
Make the food and water 'power', and give them the ability to 'harm' each other by draining power.
The girls would have a higher resource requirement to reproduce.
It'd be interesting to see over many generations what relationship patterns form between the same and opposite sex.