File sharing may pave the way for robotics Are we there yet?

By Zsolt Bitvai They are among us

Swarm of futuristic looking octopus robots are drilling their way to an underground human city. Alone, each sentinel would fail. But their power resides in their collective entity: the Swarm. Although this is just a scene from a popular sci-fi block buster, The Matrix, is a fast growing research area in the field of Artificial Intelligence. What is more, the basis for the technology is already there.

The world of body cells bears the habitat of some rather odd looking creatures: Nanobots. Molecular technology expert Dr. Eric Drexler from MIT indicates that these robots are so tiny that they may become the building blocks for any kind of material, thus could even self replicate by converting matter they come in contact with to clones, a process known as ecophagy. For not long, everything could be turned into grey goo. Is this too far fetched? Not so much, if we continue to develop the technology with such a blazing speed.

If minuscule robots could alter the world so fundamentally, what about in the scale of the Universe? NASA is contemplating a moon base to be built by an army of robots, all cooperating to achieve a common goal. It is not just America that has recognised the potential of this high-tech branch of science - Europe is a big player as well. of robots could not only be used for base establishments, but also do maintenance work around spacecraft or explore planets in the outer space. It isn't over yet. Professor Marco Dorigo from the Université Libre de Bruxelles has recently suggested discarding our conservative views on transportation - Why not could a spacecraft be simply a collection of self assembled robots?

You might think that all this is way too futuristic. I urge you to consider the following: US military is experimenting with drone vehicles that are either autonomous or operated remotely by a single person. According to robotics expert Professor Noel Sharkey from the University of Sheffield, this innovation could soon become a new standard for warfare. No people, no miseries, which means ethical responsibility of starting a war may vanish into the past along with any conscience left. Luckily, not all future scenarios result in a post apocalyptic world. Pacifists may rather prefer a handful of bots to irrigate their peach tree plantations.

The question a lot of people are asking right now is how are going to get there, if we want to get there at all? The clue lies in where we, humans, originate: Nature.

Acting collectively in a swarm is so efficient that nature has thousands of examples of it. Even though to the naïve observer ants move in a haphazard fashion, ant colonies are believed to be organised to the perfection. Schooling have long been regarded as an efficient way for fish to defend themselves against predators. If we take a look at ourselves, to the naked eye a metropolitan city seems chaotic, but on a closer look all pieces fit together. Nevertheless, with millions of road deaths a year, our traffic system is still far less efficient than that of leaf Power in numbers. hoppers, which avoid collision with more than a thousand of them a square meter. Some people even go to the extremes. Peter Russell, British spiritualist, argues that the invention of high speed internet has created a Global Brain, with human society being the body of a gigantic Earth organism, Gaia.

So nature has it. We have it. What if robot will have it...

1 Ashes to ashes

By now scientists have realised that putting some robots together in a sandbox and turning them all on will not result in . In other words, in order to induce emergent behaviour, we need to have in the first place, which is something that has evolved in nature through millions of years. Technically speaking, a swarm of robots would consists of a network of communicating agents, which can be reconfigured any time, and are able to interact with the environment. Furthermore, the swarm can achieve goals that are impossible for the individual members alone.

As with any constructions, we should start with the most fundamental element, the agent itself. What are the desired properties of an agent? They first of all are autonomous. That is, they make decisions on their own, and do not receive them from a central command dispatcher. This may sound alarming to you, but bear with me... this brings us to the second characteristic.

Agents are selfish in nature. They are willing to give resources to other agents, provided that it is for their own benefit. In other words, all agents seek is to maximise their utilities. To make things little bit more complicated, we associate actions with costs and rewards as well. Therefore, it is their marginal utility that individual peers try to maximise, and thus have things with hight utility on top of their preference list. Indeed, they are just like us...

You may wonder that this all sounds perfect in theory, but how do you go about the implementation in real life, in which not only the environment but agents' preferences change non-stop? Although the task may sound impossible for many, Andrey Markov (1859-1922), Russian mathematician, made an attempt at it. His attempt was so successful that it is now called the Markov Decision Process. In his view, we cannot foresee the future, nonetheless we are able to predict it. The prediction can occur two ways: deterministic or non-deterministic. If we assume the formal one, then a future state of the world can be reached by a transition from the present state, which inherently holds all the information we need. Could this work? Well, yes and no. In a closed environment where we know all the factors, yes. However, in open environments, like real life, things get a bit more intricate. There are so many external factors to calculate with that we can only know the probability of going into a future state.

Moreover, no-one is in control in a swarm, agents are effectively decentralised. Thus, no-one coordinates peer behaviour either. That's because agents have only limited information about the world around them. They only possess a local view of the swarm, and they only interact with their close neighbours. As a result, they cannot determine the state of the world for sure, only the probability of existing in a certain state - their Belief state . In fancy words, they apply non-deterministic Markov Decision process in a partially observable world for their behaviour. To decide what utility an action or inaction would bring to them, they associate a discount factor to the expected utility of their deed, preferring the ones which are nearer in the future to the ones that are farther away.

Now, let's imagine that we stand in a crowd at a concert and can't see anything. We ask our close neighbours what's happening, and they all start talking at the same time. In the end, we get no meaningful information out of the babel whatsoever. Likewise in a swarm, simultaneous communication could result in an overwhelming overflow and can put a heavy strain on resources. This event is called the Tragedy of Commons. To prevent it, agents make only ad-hoc connections and have a limit on their maximum number of concurrent links.

Because they are so basic elements, agents mustn't be complex entities. In fact, they are designed to be very simple in nature, and therefore they follow very basic rules. The beauty of this behaviour is that interaction between them can be described by conducting simple games. As a result, game theory thrives in multi agent systems, often dubbed MAS.

Provided that agents are autonomous and selfish, what happens when their goals are not in line with the “common” goal of the swarm? Or even worse, they have malicious intentions? Surprised? In the world of swarm robotics malicious peers present a significant risk. More on this later...

2 Are you prepared to drift with the tide?

The bigger picture

We have looked at the building blocks - the peers or agents. Next challenge is how do we get any meaningful collective behaviour out of them? Well, the answer can be quite disappointing to some: to put it bluntly, we don't. The harsh reality is that more often than not the actions of individual agents, all rigidly pursuing their own interest, cancel each other out. The tricky part is to find an algorithm that after several interactions, tend to converge to a harmonised behaviour, the Nash Equilibrium. The challenge is even harder, if this equilibrium changes all the time because of varying environment, as described in the Moving Target problem.

Provided that it is achieved, emergent behaviour would bring about an increase in the global utility of the system, which affirms that simple interactions between peers do not produce a zero sum game. The resulting complex behaviour would shift the swarm to a new level. For example, it has been shown that the cognitive power of a swarm of robots equipped with sensors can be significantly higher than that of its peers.

At this point, MAS have several advantages to conventional monolithic (central) systems. The very first one is that being distributed there is no need for a central command unit. Computationally speaking, these networks are very efficient and non-demanding, which manifests in enormous computational capability as the load is shared among the links.

Secondly, it has been confirmed that if left alone without human intervention, resource allocation in MAS tends to be Paraeto efficient, that is the best possible. This not only minimises idle time but also enables for a dynamic structure. We all know the phrase: the more the merrier. Is this true for swarm robotics? In the above examples - ant colony, fish school and metropolitan city - peers are able to join and leave the swarm whenever they want to. This openness entails tremendous flexibility. New peers also mean new resources, and virtually there is no limit on how much the group can expand, thus it is incredibly scalable. The cost increase of locating resources in a central system is exponential, while in MAS it is only logarithmic. This makes it possibles for the swarm to effectively reorganise itself in response to a changing environment.1

The weakest link

Every organisation is as strong as its weakest link. One major concern about centrally operated systems is failure. Several layers of security must be installed in order to prevent system shut downs. Losses skyrocket as million-dollar missions shipwreck unless fault is located and fixed as soon as possible. So how do multi agent systems compare in this regard? The good news is that they are virtually failure proof and extremely robust. The bad news is that this is only half the truth. On the one hand, it can be pointed out that because there is no extensive dependence on either a central unit or other peers, there is no need for prevention measures either. If a

1 It is worth mentioning the fact that MAS resembles open market economies a great deal.

3 link fails, the rest can just work around it for there are usually numerous ways to transmit information.

But what happens when an agent sends wrong information to its peers maybe because it malfunctions or perhaps has malicious intent? Pollution is difficult to filter out partly because it disrupts the system from the inside. So what can we do about it? First, identify the target. Second, isolate it, effectively externalising the problem. This is explored in detail in the Two Generals problem, where a message needs to be sent via an insecure link. It has been proven that the risk of the message being lost cannot be eliminated only minimised.

Who's the odd one? In practice, an algorithm called Byzantine Fault Tolerance (BFT) has shown compelling results with regard to tackling this issue. This way erroneous behaviour would be accepted to a certain extent. Then a ban is enforced either for a period of time or permanently. That means once the swarm notices that one of its members is out of line, first it would tolerate it, then remove it, just like a surgeon would do so to a harmful tumour in a patient's body. Note that there is no universal blacklist; it is the individual agents that do the banning.

An alternative way of assuring the integrity of a swarm is to do with machine learning. One could use BFT for multi level learning algorithms. Or just use a level 0 program. One may wonder what all these levels mean? In simple English, at level 0, an agent assumes that other peers are simply part of the environment and thus it does not distinguish between a tree and a friend. In comparison with the previous method, this is very simple to implement. In higher level algorithms, for example, an agent thinks that it is level 2 and its peers only level 1. Regarding itself to be superior, it would try to predict their behaviour and act accordingly. However, complications can arise when other peers move strikingly differently to what is expected.

Why is this such a big problem? In order to achieve (COIN) peers must communicate with each other on the same “wavelength”. If you talk in Chinese while I talk in English, the chances for us to do a coordinated activity is slim. What happens if a maverick peer comes, defies the common rules and is, for instance, irrespective as to reciprocate the resources it has requested and received from other peers. In this case, the peer is tolerated to an extent, after which it is denied further access to the network.

However, for a short period of time, it is possible for an agent to exploit the swarm. The question still remains: What does exactly constitute erroneous behaviour? Namely, a malicious peer could try to deceive others and hide its resources from them which in turn presume that it did everything it could to help. How do we discover malevolent intentions here? This issue comes from the fact that peers are not uniform and thus have different capabilities. Therefore, the quality of their contributions can vastly vary too. As a result, in higher level learning algorithms trust has a huge role in forming a common behaviour.

Future of the past

Is the world of swarm robots a good one or a bad one? Well, we don't need to think about it too much. Why? Because this world is no other than our own! So where is the swarm? The biggest network of swarming agents is right in front of us. Yes, that's right, it is in our computers, in the internet. As many as 15% of the global internet users are member of it. And that is Peer-to- Peer file sharing.

The Bittorent protocol has created the world's one of largest robot swarms, which adheres to the principles of multi agent systems. Peers are simply network nodes that have equal rights. They use resources of other peers and reciprocate. Groups are cooperative and frequently merge or disjoin. Any-one, any-time can start activity. It is immensely scalable, flexible and robust, thus very resilient. Malicious peers (e.g. copyright agencies) are one of the biggest threats to P2P networks which fights for its survival to combat them.2

2 Often called leechers

4 An interesting peculiarity of Bittorrent is the prevalence of altruism: agents who only give resources not expecting anything in return.3 There are experiments with the introduction of altruism to other applications of MAS, however, the outcomes are mixed. It is generally believed that this phenomenon is ultimately selfish in that humans exercise it to gain the reward of feeling good about themselves.

All right, but this isn't swarm robotics in the traditional sense. I want to see real, physical machines acting together - you might think. For you, there exists the I-Swarm project, funded by the Future and Emergent Technologies Programme of the EU with as many as a dozen universities participating in it throughout Europe. What's more, it has recently made the news that Jasmine Robots have been able to connect to each other and form a bridge, thereby escaping from their training field. This is something that would have been impossible for individual robots to do. These self-assembled robots may have a bright future in human colonisation of the Solar system, where bases need to be established in advance.

The designers of Jasmine aimed to create as small robots as feasible and tried to mimic the complex behaviour of ant colonies. Due to technological constraints, they cannot go on with further miniaturisation, but this field could provide opportunity for nano technology, in areas such as micro assembly, medical cleaning, cancer tumour removal and drug delivery. Experiments are undergoing with 1000+ members in a swarm. These projects imply that fast developing fields for swarm robotics are ant colony and particle swarm optimisation. The swarm escape. Let's talk now about Boids. It's a computer program designed in 1986 that simulates the of . Although the behaviour of individual animals may seem chaotic, the flock together is so effective that it can take obstacles by splitting up and then returning to formation. Birds only have a limited perception and they try to stay in alignment with their immediate neighbours. Through cohesion they can stick together. According to their algorithm, every strives to fly towards the average of all bird positions, while also trying to remain separated so as to avoid crowding. One characteristic of their emergent behaviour is that it is unpredictable, nevertheless quite life-like. Real life experiments may soon be taking place owing to a recent breakthrough in artificial robot flight research, where hummingbird imitations have been able to hover and turn in one place in the air.

The Artificial Life XI Conference is an excellent breed for genuine ideas. A group of students from the University of Southampton have managed to build a swarm of primitive robots that communicate with infra-red and are fairly affordable. They are able to divide themselves into separate groups (green team and red team) which shows that they can organise themselves, even if some members of the swarm are removed. At the same time, students at the University of Wyoming use ultra-sonic sensors for their swarm bots. They have completed successful projects where the simplistic machines found a target while having to“flow around” obstacles.

In addition, other EU funded projects include Swarmanoid, Symbrions and Replicator. Swarmanoid, supported by the Information Society Technologies framework, is a group of bots designed to build a giant robot with the help of swarm intelligence and self-assembly. Each robot is envisaged to be made up of 60 smaller ones including eye-, hands- and feet-robots.

Symbiotic Evolutionary Robot Organisms () and Robotic Evolutionary Self-Programming and Self- Assembling Organisms (Replicator) focus on creating an extensive swarm of autonomous robots, which can dock into each other and symbiotically share computational power and energy. If the goals of the individual agents are in line with the common goal, they can aggregate and physically interact with their environment as a whole. The collective is self-healing, self-optimizing, self-configuring and self-protecting both from hardware and software perspectives. What is more, with evolutionary emergent algorithms they may be able to adapt and update their software, creating new layers of emergent behaviour. Ultimately, artificial organisms may go along different evolutionary paths, only to to come in contact with each other later on, and make new variants.

3 Often called seeders

5 In conclusion, as the completion of these projects are all within reach, e.g. Symbrion and Replicator is expected to deliver by 2013, it is no exaggeration to say that once robots reach a certain plateau and will become self- defending, they may come in conflict with their creators, humans. We might as well better start preparing for the final battle for our survival as a species, and the enemy is not some extraterrestrial alien, but our creations themselves.

6 References 1. Kinematic Self-Replicating Machines, by Robert A. Freitas Jr. and Ralph C. Merkle 2. http://www.dailygalaxy.com/my_weblog/2009/03/nasa-sending-ro.html 3. http://www.guardian.co.uk/commentisfree/2007/aug/18/comment.military 4. Lewis, M. Anthony, and Bekey, George A. The Behavioral Self-Organization of Nanorobots Using Local Rules. Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems. 5. Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press, 2004. ISBN 0-262-04219-3 6. Particle Swarm Optimization by Maurice Clerc, ISTE, ISBN 1-905209-04-5, 2006. 7. Fundamentals of Multiagent Systems with Netlogo Examples, José M Vidal, 2009 8. Russell, Peter. (1982) The Awakening Earth: The Global Brain. London: Routledge & Kegan Paul. (emphasis on philosophy and consciousness) 9. http://www.symbrion.eu/ 10. www.swarmrobot.org 11. www.i-swarm.org 12. Crowcroft, Jon. Moreton, Tim. Pratt, Ian. Twigg.(2003). “Peer-to-Peer Systems and the Grid” Retrieved Jun. 24, 2006. 13. http://www.swarm-robotics.org/SAB06/presentations/dorigo-sab06-srw.pdf 14. Swarm Intelligence, Springer New York, ISSN 1935-3820 (Online) 15. Fundamentals of Computational Swarm Intelligence by Andries Engelbrecht. Wiley & Sons. ISBN 0-470-09191-6 16. Nanocomputers and Swarm Intelligence by Jean-Baptiste Waldner, ISTE, ISBN 9781847040022, 2007. 17. Swarm Intelligence: From Natural to Artificial Systems by Eric Bonabeau, Marco Dorigo and Guy Theraulaz. (1999) ISBN 0-19-513159-2, 18. http://www.impactlab.com/2008/10/22/i-swarm-project-robotic-ants-may-one-day-build-on-mars/ 19. A Review of Studies in Swarm Robotics, Levent BAYINDIR and Erol SAHIN 20. Joseph, Lawrence E. (2007). Apocalypse 2012. New York: Broadway. p. 6. ISBN 978-0767924481. 21. Foundation of Peer-to-Peer Computing , Special Issue, Elsevier Journal of Computer Communication, (Ed) Javed I. Khan and Adam Wierzbicki, Volume 31, Issue 2, February 2008 22. Ramesh Subramanian and Brian Goodman (eds), Peer-to-Peer Computing: Evolution of a Disruptive Technology, ISBN 1-59140-429-0, Idea Group Inc., Hershey, PA, USA, 2005. 23. Dimitrios K. Vassilakis, Vasilis Vassalos, "Modelling Real P2P Networks: The Effect of Altruism," p2p, pp.19-26, Seventh IEEE International Conference on Peer-to-Peer Computing (P2P 2007), 2007 24. http://cs.eou.edu/CSMM/surangah/research/alumni.ppt 25. http://news.bbc.co.uk/1/hi/7549059.stm

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