Or at least that's not too far off from what University of Vermont roboticist Josh Bongard has discovered, as he reports in the January 10 online edition of the Proceedings of the National Academy of Sciences. In a first-of-its-kind experiment, Bongard created both simulated and actual robots that, like tadpoles becoming frogs, change their body forms while learning how to walk. And, over generations, his simulated robots also evolved, spending less time in "infant" tadpole-like forms and more time in "adult" four-legged forms. These evolving populations of robots were able to learn to walk more rapidly than ones with fixed body forms. And, in their final form, the changing robots had developed a more robust gait -- better able to deal with, say, being knocked with a stick -- than the ones that had learned to walk using upright legs from the beginning. "This paper shows that body change, morphological change, actually helps us design better robots," Bongard says. "That's never been attempted before."

Bongard's research, supported by the National Science Foundation, is part of a wider venture called evolutionary robotics. "We have an engineering goal," he says "to produce robots as quickly and consistently as possible." In this experimental case: upright four-legged robots that can move themselves to a light source without falling over. "But we don't know how to program robots very well," Bongard says, because robots are complex systems. In some ways, they are too much like people for people to easily understand them. "They have lots of moving parts. And their brains, like our brains, have lots of distributed materials: there's neurons and there's sensors and motors and they're all turning on and off in parallel," Bongard says, "and the emergent behavior from the complex system which is a robot, is some useful task like clearing up a construction site or laying pavement for a new road." Or at least that's the goal. But, so far, engineers have been largely unsuccessful at creating robots that can continually perform simple, yet adaptable, behaviors in unstructured or outdoor environments. Which is why Bongard, an assistant professor in UVM's College of Engineering and Mathematical Sciences, and other robotics experts have turned to computer programs to design robots and develop their behaviors -- rather than trying to program the robots' behavior directly. His new work may help.

Using a sophisticated computer simulation, Bongard unleashed a series of synthetic beasts that move about in a 3-dimensional space. "It looks like a modern video game," he says. Each creature -- or, rather, generations of the creatures -- then run a software routine, called a genetic algorithm, that experiments with various motions until it develops a slither, shuffle, or walking gait -- based on its body plan -- that can get it to the light source without tipping over. "The robots have 12 moving parts," Bongard says. "They look like the simplified skeleton of a mammal: it's got a jointed spine and then you have four sticks -- the legs -- sticking out." Some of the creatures begin flat to the ground, like tadpoles or, perhaps, snakes with legs; others have splayed legs, a bit like a lizard; and others ran the full set of simulations with upright legs, like mammals. And why do the generations of robots that progress from slithering to wide legs and, finally, to upright legs, ultimately perform better, getting to the desired behavior faster? "The snake and reptilian robots are, in essence, training wheels," says Bongard, "they allow evolution to find motion patterns quicker, because those kinds of robots can't fall over. So evolution only has to solve the movement problem, but not the balance problem, initially. Then gradually over time it's able to tackle the balance problem after already solving the movement problem." Sound anything like how a human infant first learns to roll, then crawl, then cruise along the coffee table and, finally, walk? "Yes," says Bongard, "We're copying nature, we're copying evolution, we're copying neural science when we're building artificial brains into these robots." But the key point is that his robots don't only evolve their artificial brain -- the neural network controller -- but rather do so in continuous interaction with a changing body plan. A tadpole can't kick its legs, because it doesn't have any yet; it's learning some things legless and others with legs. And this may help to explain the most surprising -- and useful -- finding in Bongard's study: the changing robots were not only faster in getting to the final goal, but afterward were more able to deal with new kinds of challenges that they hadn't before faced, like efforts to tip them over. Bongard is not exactly sure why this is, but he thinks it's because controllers that evolved in the robots whose bodies changed over generations learned to maintain the desired behavior over a wider range of sensor-motor arrangements than controllers evolved in robots with fixed body plans. It seem that learning to walk while flat, squat, and then upright, gave the evolving robots resilience to stay upright when faced with new disruptions. Perhaps what a tadpole learns before it has legs makes it better able to use its legs once they grow. "Realizing adaptive behavior in machines has to date focused on dynamic controllers, but static morphologies," Bongard writes in his PNAS paper "This is an inheritance from traditional artificial intelligence in which computer programs were developed that had no body with which to affect, and be affected by, the world." "One thing that has been left out all this time is the obvious fact that in nature it's not that the animal's body stays fixed and its brain gets better over time," he says, "in natural evolution animals bodies and brains are evolving together all the time." A human infant, even if she knew how, couldn't walk: her bones and joints aren't up to the task until she starts to experience stress on the foot and ankle. That hasn't been done in robotics for an obvious reason: "it's very hard to change a robot's body," Bongard says, "it's much easier to change the programming inside its head."

Still, Bongard gave it a try. After running 5000 simulations, each taking 30 hours on the parallel processors in UVM's Vermont Advanced Computing Center -- "it would have taken 50 or 100 years on a single machine," Bongard says -- he took the task into the real world. "We built a relatively simple robot, out of a couple of Lego Mindstorm kits, to demonstrate that you actually could do it," he says. This physical robot is four-legged, like in the simulation, but the Lego creature wears a brace on its front and back legs. "The brace gradually tilts the robot," as the controller searches for successful movement patterns, Bongard says, "so that the legs go from horizontal to vertical, from reptile to quadruped. "While the brace is bending the legs, the controller is causing the robot to move around, so it's able to move its legs, and bend its spine," he says, "it's squirming around like a reptile flat on the ground and then it gradually stands up until, at the end of this movement pattern, it's walking like a coyote." "It's a very simple prototype," he says, "but it works; it's a proof of concept."

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Vermont. The original article was written by Joshua E. Brown.

Most theoretical linguists, including the noted researcher Noam Chomsky, argue that people have little more than a "language organ" -- an inherent capacity for language that's activated during early childhood. On the other hand, researchers like Dr. Roni Katzir of Tel Aviv University's Department of Linguistics insist that what humans can actually learn is still an open question -- and he has built a computer program to try and find an answer.

"I have built a computer program that learns basic grammar using only the bare minimum of cognitive machinery -- the bare minimum that children might have -- to test the hypothesis that language can indeed be learned," says Dr. Katzir, a graduate of the Massachusetts Institute of Technology (where he took classes taught by Chomsky) and a former faculty member at Cornell University. His early results suggest that the process of language acquisition might be much more active than the majority of linguists have assumed up until now. Dr. Katzir's work was recently presented at a Cornell University workshop, where researchers from fields in linguistics, psychology, and computer science gathered to discuss learning processes.

Able to learn basic grammar, the computer program relies on no preconceived assumptions about language or how it might be learned. Still in its early stages of development, the program helps Dr. Katzir explore the limits of learning -- what kinds of information can a complex cognitive system like the human mind acquire and then store at the unconscious level? Do people "learn" language, and if so, can a computer be made to learn the same way? Using a type of machine learning known as "unsupervised learning," Dr. Katzir has programmed his computer to "learn" simple grammar on its own. The program sees raw data and conducts a random search to find the best way to characterize what it sees. The computer looks for the simplest description of the data using a criterion known as Minimum Description Length. "The process of human learning is similar to the way computers compress files: it searches for recognizable patterns in the data. Let's say, for instance, that you want to describe a string of 1,000 letters. You can be very naïve and list all the letters in order, or you can start to notice patterns -- maybe every other character is a vowel -- and use that information to give a more compact description. Once you understand something better, you can describe it more efficiently," he says.

His early results point to the conclusion that the computer, modeling the human mind, is indeed able to "learn" -- that language acquisition need not be limited to choosing from a finite series of possibilities.

While it's primarily theoretical, Dr. Katzir's research may have applications in technologies such as voice dialogue systems: a computer that, on its own, can better understand what callers are looking for. A more advanced version of Dr. Katzir's program might learn natural language grammar and be able to process data received in a realistic setting, reflecting the manner in which humans actually talk. The results of the research might also be applied to study how we learn to "read" visual images, and may be able to teach a robot how to reconstruct a three-dimensional space from a two-dimensional image and describe what it sees. Dr. Katzir plans to pursue this line of research with engineering colleagues at Tel Aviv University and abroad.

"Many linguists today assume that there are severe limits on what is learnable," Dr. Katzir says. "I take a much more optimistic view about those limitations and the capacity of humans to learn."

Still, Bongard gave it a try. After running 5000 simulations, each taking 30 hours on the parallel processors in UVM's Vermont Advanced Computing Center -- "it would have taken 50 or 100 years on a single machine," Bongard says -- he took the task into the real world. "We built a relatively simple robot, out of a couple of Lego Mindstorm kits, to demonstrate that you actually could do it," he says. This physical robot is four-legged, like in the simulation, but the Lego creature wears a brace on its front and back legs. "The brace gradually tilts the robot," as the controller searches for successful movement patterns, Bongard says, "so that the legs go from horizontal to vertical, from reptile to quadruped. "While the brace is bending the legs, the controller is causing the robot to move around, so it's able to move its legs, and bend its spine," he says, "it's squirming around like a reptile flat on the ground and then it gradually stands up until, at the end of this movement pattern, it's walking like a coyote." "It's a very simple prototype," he says, "but it works; it's a proof of concept."

However, the usefulness of such ad hoc networks can be offset by vulnerabilities. Like any network, a MANET can be susceptible to attack from people with malicious intent. Illicit users might, for instance, hook up to such a network and impersonate a legitimate user in an effort to access data and logins to which they do not have approval, others may implant harmful software, malware. The most worrying form of attack, however, is the distributed denial of service (DDoS), which effectively swamps the network with random data requests until it is overwhelmed. This might simply be for the sake of causing disruption but it a DDoS attack might also be used to hook into security loopholes and quickly and transparently implant malware to harvest logins, bank details and other private information. Now, Yinghua Guo of the Defence and Systems Institute, at the University of South Australia, in Mawson Lakes and Sylvie Perreau of the Institute for Telecommunications Research, in Mawson Lakes, have developed a computer algorithm that runs on the network and rapidly, within 10-22 seconds, identifies when a DDoS is initiated based on the new, unexpected pattern of data triggered by the attack. The false positive rate is very low and it allows the system to trace the illicit activity back to the main nodes from which it is originating and to deny them access to the network, so thwarting the attack very quickly.

The researchers say that their technique can halt 80% of the DDoS attack traffic and so allow users to continue using their devices almost as normal and to block the window of opportunity through malware might be implanted during such an attack. More importantly, from a computer science point of view is that the study provides a model framework on which better security systems might be built for MANETs and other networks.