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McLurkin's was by far the most exciting presentation of Day2 at the IdeaFestival. Surrounded by buzzing robots that he eventually made perform in a real robotic orchestra, James McLurkin, a PhD student at MIT Computer Science and Artificial Intelligence Laboratory, delivered a highly engaging talk about distributed robotics and swarm behavior to a jam-packed audience (by the way, when was the last time that a talk on robotics and swarm behavior was sold out in a place like Louisville? Are ordinary Americans rediscovering the joy of science?).

McLurkin works in an extremely complex but exciting field of “distributed robotics” that currently attracts a lot of attention from many private and government players. His focus is on building software that produces complex group behavior from the interactions of many simple individuals.

His work has its theoretical roots in swarm intelligence, which is an artificial intelligence technique based around the study of collective behavior in decentralized, self-organized systems. It's most commonly found in nature – think about ant colonies, bird flocking, animal herding, fish schooling, and many other examples. All these complex behaviors emerge from local interaction between numerous much simpler agents, without any system of centralized control.

During the last few hundred million years, nature has perfected such interactions. Now, scientists like McLurkin want to get a better understanding of how these biological processes work and apply this knowledge to programming robots to do complex tasks in groups. Perhaps, this is the ultimate interpretation of the Wisdom of Crowds thesis: individuals don't have to be smart to produce very smart group outcomes. Did somebody mention Wikipedia?  

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McLurkin is still in his thirties, but one already spots some gray hair on his forehead. An energetic man with a spicy sense of humor, which he got a chance to exhibit during his talk ("the American foreign policy -- or the lack thereof" was just one pearl), he stands out from the rest of the festival's speakers by using a Dell laptop, not an Apple. One is tempted to ask if he's funded by Dell, but nobody dares to ask that very pertinent question.

After a brief intro, he gets started. His presentation “Dances with Robots” is subtitled “ the story of one engineer, 112 little robots, and the toys, insects, and Star Wars movie that made it all possible”. This is enough to signal to everyone in the audience that this isn't going to be another "death by PowerPoint".

What a better way to kick off your presentation than to talk about the end of the world? McLurkin does exactly that, providing a short excursus into the end of the world as imagined by the American pop culture. Hollywood always has the final say as to how we are all going to die.

Then McLurkin pulls up a slide with three laws that should be observed to prevent the planet by being attacked by the robots (referenced to the Handbook of Robotics -- or was it Isaac Asimov?). “Well, robots don't know how to read, so those laws are not particularly useful”, smiles McLurkin.

Robots are not even smart enough to travel from the stage where McLurkin stands to the audience: they would either get trapped in some wires or collapse to the floor. For all the talk about robotics, today an average squirrel can still do more than any robot, says McLurkin.

McLurkin points to a number of philosophical, not just engineering problems, in his field. Problem number one is that we don't know what intelligence is nor how to define it. Should we subject the robots to some upgraded version of the Turing Test (it says that if a judge can't tell whether he is talking to a machine or a person, the machine passes the intelligence test)?

Can intelligence emerge from interactions of unintelligent components? -- is a second philosophical question that McLurkin asks. As we are all built from molecules, continues McLukrin, either intelligence is something that results from interactions or molecules are intelligent.

The third and final question is whether an intellect needs a body. Can a brain in a vat understand and experience the world without anything to relate to? Can we build such an intellect?

That slide with the three philosophical questions is subtitled ”things that make you go “hmmm”, and one can hear half of the audience “hmming”.

Having finished with the philosophy, McLurkin gives a brief overview of the previous efforts to mass-build robots, presenting quite a few models: from iRobot Roomba to Honda Asimo to iRobot Packbot-- all of them having different looks and different functionality. And, of course, NASA's successful launch of two robots onto Mars (“not a very pleasant environment to spend two years in”, jokes McLurkin).

Quite naturally, McLurkin makes a transition to his own work. He has about 112 robots in his arsenal and he is trying hard to make them work together. In his view, robots are best at jobs that are dangerous, dirty, or dull. “What if we sent 20 robots to work in hot spots around the world? What if we sent 200 robots to look for surivors after an earthquake? What if sent 2,000 robots to explore mars”, McLurkin fires his questions at the audience.

It's this last question he wants to address with his on-stage demonstration. McLurkin turns to a few dozen robots that he has on stage (he controls them via remote control). As a starter, he asks the robots to form a line; surely enough, they do. Next, he orders the robots to spread out – they perform this also.The demonstration proceeds quite smoothly.

One thing that the robots don't know yet is how to define boundaries of the network, so they often spread out from the center and then get disconnected. The robots can communicate via one another (they know the neighbors, but don't know about everybody else) but not with everybody at once. So if they need to find a robot that is not in their neighborhood, they must relay the info via their neighbors.

To find the answer, they go around and query one another to find the result. The robot that is searching just goes around and asks a robot next to him. The network reconfigures in real-time and the robot is going to move around the network until it finds the robot in question. They can also form protective areas/fences. And, of course, they can also leave the planet in orderly fashion, so McLurkin has his robots leave the stage by ID. Two special robots know they are special and the rest know that they are ordinary. So they query all neighbors about their id and then place themselves between the two neighbors – one that has a greater id than them and one that has a lower id than them-- until the whole “squad” is arranged.

After the demonstration is over, McLurkin asks how exactly to program 2,000 robots? Natural systems – and in particular in swarm intelligence – can provide insight into these extremely complex programming problems.

Thus, one of the areas that McLurkin examines very closely is nectar collection in bees – how foraging bees communicate with workers beers in the hive (Thomas Seeley, a biologist at Cornell University, has been doing some very interesting work on this topic). With thousands of workers in a hive, honeybees have learned to bypass their individual judgements and do what's best for the colony.

Getting as much information about the processes that drive this decision-making in bees would allow to present the bee algorithm in a software-like way– and McLurkin's question is whether he can run this software on his robots. This is what McLurkin dubs “beeware” -- the one that doesn't need to get debugged, as the nature has already done its job of “debugging” over the last 120 million years.

Communication among ants is actually not as complicated as it seems. They do so by leaving pheromone trails whenever they find food, so that other ants follow the trails when they find them, instead of traveling and searching for food randomly. The ants are foraging in a globally optimal fashion – by exploring the closest food source. This has acquired the name of ant colony optimization (which McLurkin didn't name but talked about a bit). So, in short, the ants are following the trail that has the strongest odor.

The magic of complexity, according to McLurkin, is how simple local interactions can form complex group behaviors. This is pretty much how insect communities work. Things like this, says McLukrin, are called distributed systems. This is a very interesting research area, but it makes a robot swarm difficult to program, says McLurkin.

So he wants to do something different. Although at the moment we mostly focus on writing individual robotic software, McLurkin wants to write group software. So he first takes group software, turns it into robot software, then turns it into physical robat actions, and then it becomes physical group actions again (it wasn't the easiest part of McLurkin's talk to understand).

He then proceeds to show a short video of a few dozen bots exploring a room and searching for an orange ball that was hidden in it (imagine that it's a land-mine or a bomb, not an orange ball, that they need to fine—and you would understand why the government agencies are so excited about McLurkin's work). There were four types of robots and each was playing a special role in the experiment (they are all operated and commanded via SwarmOS, a special operating system that McLurkin created especially to command the robots). The robots succeeded in their mission and safely left the room; only one robot couldn't return. “A success”, smiles McLurkin.

The key to understanding the thought process behind McLurkin's robotics is what is called “distributed averaging”. Think about software than runs on multiple computers and interacts to form a group result. To illustrate his point, McLurkin asked for 8 volunteers from the audience, gave each of them a piece of paper with a number, split them in pairs, handed them calculators and asked them to calculate the average and then change the partners and repeat the calculation. After a few rounds, almost everybody arrived at the same number – although they never talked to the whole group, just to their round partners.

Bees constantly engage in similar processes. Honeybee workers share food all the time, precisely thanks to their computation of a global average. This lets an individual worker know when the hive is hungry by measuring when she is herself is hungry. The assumption is simple: “When I am hungry, the rest of the hive is hungry”. McLurkin admiringly talks about making a local decision based on global information.

McLurkin finishes his presentation by a short self-PR stint, showing a number of press clippings and magazine covers, not to forget numerous souvenirs that robots-obsessed high-school students flood him with.

“A master in the art of living draws no sharp distinction between work and play”, concludes McLurkin as he has his robots orchestra play a farewell song.