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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."
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.
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
To use the term "learned" for a consequence of evolution to what seems to me to be a Genetic Algorithm seems mis-leading.
"Learned" is a perfectly good description for altering a neural network to have the "learned" behavior regardless of the method. GA-guided-Neural-Networks means you're going to be using terminology from both areas, but that's just one method of training a network and isn't fundamentally different from the many other methods that are all called "learning". But you wouldn't say about those other methods that they "evolved", while about GA-NN you could say both.
Isn't this to be expected?
It's expected that the GA will find good solutions. Part of what makes them so cool is that the exact nature of that solution isn't always expected. Who was to say whether the machines would learn to turn off the light near food, or to turn on the light when they know they're not near food to lead other robots on a wild goose chase? Or any other local maximum.
The enemies of Democracy are
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.