On hunting and killing from the arm-chair
While a leak in the Independent newspaper informs that Tony Blair has decided to upgrade the UK's Trident nuclear deterrent, it appears that North Korea is nearing completion of its first nuclear weapon. Last week also saw the anniversary of the World War I Gallipoli campaign, today's warriors seem to be made of a very different metal.
Many might find the below items worrying.
Robot to take over border guard mission
Target practice and hunting from the arm-chair
Robot soldiers to fight on the battlefield within the next decade
Advancing Autonomy for Combat Operations
The Defense Ministry said Friday that it will introduce a robot surveillance system for the defense of front line areas along the inter-Korean border by 2011.
The six-year project was initiated last October to strengthen surveillance along the heavily fortified border with North Korea, ministry spokesman Shin Hyun-don told reporters.
``The gist of this project is to transform the current guard and observation mission on fronts conducted by soldiers into one by a robot system,’’ Shin said. ``We will comprehensively review requirements of operational capability before implementing it.’’
The ministry will study the project’s effectiveness this year in cooperation with the Agency for Defense Development and the Ministry of Information
and Technology, the spokesman said.The ministry will select a business contractor next year and the project will be gradually implemented from 2007, after a feasibility study between 2006 and 2007.
One option is to upgrade the Aegis Robot (intelligence surveillance and combat robot) that was deployed to Iraq last October, ministry officials said. The robot is equipped with a temperature and image sensor that detects and tracks a suspected target. It also has an auto-firing capability. Currently, two robots are being operated in the northern Iraqi city of Irbil, where some 3,500 South Korean troops are stationed for the reconstruction mission.
``The Aegis Robot has a shortcoming in that a firing should be controlled by a staff member of the commander and control center, and it’s also questionable if it can be applied to the eastern fronts that have jagged areas,’’ Shin said. ``We will investigate these matters more extensively.’’
The cost of installing the system along the 253 kilometers-long barbed wirer fence is estimated at some 20 billion won ($1.9 billion), ministry officials said.
In short- and mid-term measures to boost security along the inter-Korean border, the ministry will replace outdated military equipment and barbed wire fences, they said.
Last October, three holes were found in barbed-wire fences in the midsection of the land border, drawing criticism about the military’s security capabilities along the Demilitarized Zone.
South and North Korea technically remain at war since the 1950-53 Korean War ended with an armistice, not a peace treaty. Nearly two million troops from both sides still face each other across the border.
From the company
website
LIVE-SHOT is similar to a trip to the rifle range with one very notable exception. Everything is done through a computer and the Internet. A paid membership will allow for access to the range viewing camera(s) at any time. Members can then schedule a reserved session time which allows exclusive control of the shooting system to fire at a choice of various reactive targets. Please note that the shooting range is an outdoor facility located on a secluded ranch in the Texas hill country. Please take this into consideration while shooting is taking place, as weather can affect accuracy.
At all times during a shooting session, someone is at the shooting station and is available to answer questions (e-mail, instant messaging), provide assistance, and ensure a quality experience. This person has the ability to override the firing mechanism of the system to minimize the chance of a dangerous or illegal discharge from occurring.
We are currently working on a very comfortable, ADA compliant blind which will house the LIVE-SHOT shooting system. Once this and the perimeter fencing are completed, will we be able to offer a unique computer assisted hunting opportunity. Disabled and handicapped hunters, as well as others who would like to try this type of hunting, will be able to use our system. This is offered in addition to the traditional hunting methods that will be available at the ranch. For those interested in coming to Texas for a hunt, lodging and meals are available in nearby Rock springs. Transportation is available to and from the ranch and pick up form San Antonio international airport can be provided. Listed are some of the species which will be available on a year round basis.
Aoudad (Barbary Sheep) Native to Northern Africa. These animals vary in coloration from a light tan to almost a dark brown. Both males and females have horns that grow up and back then curl in towards the neck and head. Long guard hairs grow as a mane and chaps that give these animals an impressive appearance. Many trophies are displayed in half body positions to show off this distinctive feature. Horn lengths typically run 26-32 inches for trophy class animals.
Blackbuck Antelope. Originally from India. Males grow V-shaped ringed horns which corkscrew out from the base. Horn length varies in adults from 13 to 23 inches. Look for trophy animals to have the beautiful black and white coloration with horn lengths from 16 to 20 inches. Blackbuck meat is delicious and ranked among the top three in exotic venison taste tests.
SHEEP. Corsican, Mouflon, and crosses. A wide variety of sheep species can be seen. Look for color and horn combinations that you prefer.
Wild Hog. Although trophy boars have been put on the ranch for hunting purposes, do not be surprised to see multiple hogs of varying sizes. These animals have become well established and will appear anywhere there is available food and cover. A large animal with protruding tusks will make for a trophy worthy of display. Large males will probably have a gamey flavor while average sized animals from 60 to 120 pounds are excellent table fare.
Other antlered species like axis, fallow, and red stag will be available on a limited basis. If you are interested in one of these or possibly another species not listed, contact us and we’ll be glad to assist you in providing the chance at the trophy of you dreams. Meat processing and taxidermy work are available from independent providers and shipping is available worldwide. Subject to the restrictions and regulations of the final destination. Links to these providers are coming soon.
There is a guide fee of $150.00 per hunt in addition to the harvest fee. Each species will vary in price and a price list will be posted when hunting becomes available. When an animal is harvested, a deposit is required for taxidermy and meat processing. The taxidermy deposit is $175.00, $25.00 is retained by us for preparation and $150.00 is given to the taxidermist to start the mounting process. An additional $60.00 is charged for meat processing. If you elect to keep the meat for your consumption, then this deposit is passed on to the meat processor. If you are unable to use the meat for yourself, a donation can be made to either a charitable orginization or to an animal orphanage. The $60.00 charge is then used as a contribution to these programs.
Is this like playing a video game?
No, this is real. What you see on your screen thru the camera is what is there. When you activate the fire control, you are sending a signal to the firing mechanism which discharges a round. You control the camera and firearm.
Why is there someone on site during a session?
Several reasons. Foremost is safety. The on site personnel have the same view as you and have the ability to override the firing signal if the firearm is aimed at something not supposed to be shot. A bird flying into the area for example. They are there to answer questions via e-mail, instant messaging, or web cams. They reset the system after each session and will be the primary scorer in competitions.
How come we cannot see the shooting range on your website?
Camera control and viewing is limited to paid members only. Once you join, you will be given instructions and password(s) to allow for viewing and then be given a different password to control the firearm/camera system during your scheduled session.
Who else has control of the cameras and firearms while I’m online?
At any given time, there are viewing only cameras that can be accessed by members at any time. If multiple members try to access these cameras at the same time, then each member will be allowed a limited amount of control time while they are logged on.
During a reserved shooting session, exclusive control of a separate camera/firearm system is given to the member. The on site person is able to have the same view of the system and is only able to shut of the firing mechanism and manually safe the firearm.
What if I’m interested in hunting in Texas?
Visit our hunting page on this site for more information on different opportunities for a hunt at our ranch.
Pentagon to pay millions for Scots’ robot soldiers
From the Glasgow Herald
By Neil Mackay
ROBOT soldiers manufactured to kill enemy troops have been designed for the Pentagon by a tiny Glasgow computer company which is set to make millions from the deal. Essential Viewing says the technology comes straight from the world of science fiction. Chief executive Simon Hardy said the technology had its nearest equivalent in the Star Wars movie Attack Of The Clones.
In the film, armies of robots are able to fight running battles, making human casualties, for the side possessing the technology, a thing of the past.
The equipment refined by Essential Viewing will see robot vehicles equipped with an array of video cameras and weaponry. The images picked up by the robots will be instantaneously relayed back to military commanders who can then move the robot or order it to shoot at targets.
With current technology, which attempts to relay live video images between one side of the globe and the other, there is a significant delay – making it impossible for the military to use a robot with vision in the battlefield effectively.
However, with the Essential Viewing system there is effectively no delay: military commanders see exactly what the robot sees at exactly the same time.
The transmission is truly live, said Hardy. Which means a person watching what the robot sees can make it interact with the environment around it. Using current technology, if a robot sees a target then the delay means that it is impossible for the military commander to make the robot follow it accurately or target it properly.
With our technology, it is as if the military commander is in the battlefield himself. Our company, which has just 15 guys in Glasgow, is the first to crack this technology and we are ahead of the world.
Wars are becoming increasingly costly in political terms because of human casualties. Rising death tolls can even bring down governments. There has been a huge push to get technology to the stage where humans can be taken out of the frontline.
Our technology means you can steer an unmanned tank, plane, boat or a robot on the frontline from a military base behind the lines or in another country. The intention is to create wars without humans. We are going in the direction of Stars Wars and Attack Of The Clones-style combat.
The Glasgow technology has been designed under the US government’s $15 billion Future Combat System programme. Some of the research was conducted at Sandia National Laboratories in New Mexico, which is managed by the arms company Lockheed Martin under the auspices of the Pentagon.
According to Hardy, there is similar interest from the Ministry of Defence in the work his company is conducting. Although he is unable to disclose details of the full value of the work as the technology is classified, it is thought to run into multi-millions of pounds.
We are taking part in a revolution in warfare, he said. This could make war much less likely as regimes which threaten the allies may see this as a deterrent as allied armies will lose very few casualties.
Essential Viewing’s technology has already been tested in the US by the military driving a robot around New York. Hardy said the robot looked like a multi-armed sophisticated bomb disposal vehicle, adding that the technology was down to some very scary maths.
This is the product of years of research, he said. We’ve been working on this for six years and have no close competitors.
Armed robot vehicles could be dropped out of planes and left to roam enemy territory to scout for targets which they could attack themselves or pin-point them with lasers for bomber planes to take out.
This will allow the military to use robots to do almost anything that a human can do on the battlefield, said Hardy. But the benefit will be that there will be no humans there. Hardy joined the company in early September 2001, intending to use the technology to send live images to mobile phones – but then September 11 happened.
The bottom dropped out of the entertainment market at that point, says Hardy, and the defence and security industries surged. We were able to switch our technology to a military application. The technology can also be used for law enforcement covert surveillance, recovering victims in natural disaster and tackling domestic terrorism.
From News target dot com
30/4/2005
The Pentagon predicts that robots will be a key force in the battlefield in a decade’s time, where the machines would be able to hunt and kill enemies. The army plans to invest tens of billions into an automated fighting force, which will drive defence budgets up by 20% to $502.3 billion in 2010. Be sure to read the related article,
U.S. Army tests battlefield
robot armed with pump action shotgun; bring
on the Terminators!
The same company that makes those cute little household vacuuming robots now has a military robot that is equipped with a pump action shotgun capable of firing shotgun rounds and presumably killing enemy combatants (or anyone who happens to be standing in front of the 'bot). The robot is called the Pacbot, and it has already seen action in Iraq. The Pacbot weighs about 40 pounds, and is propelled by heavy-duty tracks. It also has chemical sensors that detect nuclear, biological, and chemical contaminants. It's currently being tested by the 29th Infantry Regiment at Fort Benning, Georgia.
Of course, the big story here is not that robots are being used in Iraq or tested by the U.S. Army -- the big news is that they are being equipped with lethal weapons. Up until now, robots have always been limited to support roles, such as carrying equipment, sniffing out bombs, or performing remote detection of nuclear, biological, or chemical contaminants. But now there are Army robots with shotguns. Next up? Robot-controlled Hummers that can't drive straight, but can still shoot. Once they get the bugs out of the software, they'll even be able to limit their shooting to the enemy rather than just randomly firing off shotgun rounds at anything that moves.
To give you some perspective on why I think this is a horrific and yet important milestone in the use of military robots, you have to go back to some of the articles I've already written about this. In previous articles, I've talked about the Pentagon's obvious desire to create an army of robotic killing machines. It might sound like a bit of science fiction at first, but stay with me on this, and I'll tell you why this makes perfect sense, at least from the distorted point of view of the Pentagon. (Which, by the way, somehow managed to make the wreckage of an entire jet liner vanish within minutes after the 9/11 Pentagon attack...)
For one thing, the United States loves to engage in military action around the world. We can debate the effective use of military force in the global theater all day long, but the fact is that the U.S. under the Bush administration has bypassed diplomatic actions and gone directly to the use of deadly force to accomplish what are essentially diplomatic goals. Negotiations are a lot more effective when you have a gun in your hands, apparently. Or, in the near future, a gun in your robotic claws (that way, you can stand back at a safe distance in case the software goes haywire again...)
A new era of robot warfare has been launched with the US Army employing 100 TALON robots equipped with off-the-shelf chemical, gas, temperature, and radiation sensors for deployment in Iraq and Afghanistan. The explosive ordnance disposal (EOD) robots are to be used for a variety of missions ranging from clearing live grenades to neutralising mines in shallow water, and can be adapted for small mobile weapons systems (SMWS) for force protection.
The TALON robots, built by US company Foster-Miller, have already performed over 10,000 explosive ordnance disposal (EOD) missions. They are rugged, all-weather platforms capable of manoeuvring in desert or beach sand, snow, water, grassy or wooded terrain, and inside buildings. TALON robots can climb stairs and sustain falls and right themselves. They have been lowered into landing zones by helicopters, dropped from moving vehicles and launched offshore to reach underwater targets.
The TALON is a general-purpose modular robot with a versatile 64-inch pincer arm. It is controlled through RF or a fibre optic link from an attaché-sized operator control unit (OCU) or wearable OCU. On the ground the TALON can reach a vehicle speed of 6.6 km/h and last a four-hour run time. Mounted on the TALON robot are:
• Smiths APD 2000 advanced
portable chemical agent detector.
• Draeger Multiwarn II gas detector.
• Raytek Raynger MX4+ temperature sensor.
• Thermo FH 40 GL radiation detector.
• Draeger Multiwarn II gas detector.
• Raytek Raynger MX4+ temperature sensor.
• Thermo FH 40 GL radiation detector.
The APD 2000 detects chemical warfare agents, gamma radiation and irritants such as pepper spray and mace. The Draeger Multiwarn II can measure more than 50 gases including carbon dioxide, methane, propane, fuels and solvents. The Raytek Raynger MX4+ is the most advanced portable thermometer in the industry and the only one designed with precise infrared beam tracking. It is accurate to within 1 deg C and can be used to remotely sense the heat of a fire behind a closed door. The Thermo FH 40 GL takes measurements between 30 kilo-electron-volts and 1.3 mega-electron-volts and can record a radiological exposure rate from 1 micro-Roentgen per hour to 10 Roentgens per hour.
Institute
for Defense and Government Advancement
Military Robotics - Gov't/Military Staff
Advancing Autonomy for Combat Operations
April 19 - 20, 2005 ·
Georgetown University Conference Center (and Hotel), Washington, DC
Military Robotics - Gov't/Military Staff
Advancing Autonomy for Combat Operations
April 19 - 20, 2005 ·
Georgetown University Conference Center (and Hotel), Washington, DC
International Journal of
Advanced Robotic Systems
Self-Organization
and Human Robots
Introduction
Humans are rather funny things, we often tend to imagine that we are so ‘special’, so divorced by our supposed ‘intelligence’ from the influences of the ‘natural world’ and so unique in our ‘abstracting’ abilities. We have this persistent delusion, evident since ancient Greek times, that we are ‘rational’, that we can behave as ‘disinterested observers’ of our world, which manifests in AI thought today in a belief that, in a like manner, we can ‘design’, God like, from afar, our replacements, those ‘super-robots’ that will do everything that we can imagine doing, but in much ‘better’ ways than we can achieve, and yet can avoid doing anything ‘nasty’, i.e. can overcome our many human failings - obeying, I suppose, in the process, Asimov’s three ‘laws of robotics’.
Such human naiveté proves, in fact, to be quite amusing, at least to those of us ‘schooled’ in AI history. When we look at the aspirations and the expectations of our early ‘pioneers’, and compare them to the actual reality of today, then we must, it seems, re-discover the meaning of the word ‘humility’. Enthusiasm, good as it may be, needs to be moderated with a touch of ‘common sense’, and if our current ways of doing things in our AI world don’t really work as we had hoped, then perhaps it is time to try something different (Lucas, C., 1999)?
From Control to Freedom
The traditional AI approach, being a top-down process, echoes the general behaviours seen in our world today, which attempt to centralise power and to have one ‘designer’ (or a small group of them) create a ‘robot’ or ‘system’ based upon a specification of some sort or another. In other words we need to first decide what ‘someone’ wants to achieve and then to implement or to impose a way of arriving there. Unfortunately the success of this method has been rather slight in practice, we still don’t know enough about the basis of intelligence to ‘design it’ effectively - especially if we wish to mimic what humans actually do ‘well’, rather than what they do ’badly’ (which we can, rather irrelevantly, just ‘manage’, it seems, to do artificially!). As a way of overcoming the limitations of this ‘outsider’ method, an alternative has been proposed, i.e. the subsumption architecture of (Brooks, R., 1990). Here we concentrate our attention on a number of relatively simple operations, for example ‘moving forward’ or ‘turning’. Each of these is implemented in an autonomous module and these are then arranged into a layered hierarchy, with the most ‘primitive’ at the bottom.
Each module can then inhibit the higher (more valued) modules, whenever their lower function is needed or is ‘necessary’, in other words we have ‘priority interrupts’. In this way we can avoid the need to plan out exactly what should happen in every possible scenario, instead we leave it to the environmental feedback to ‘select’ (evolutionary fashion) which ‘specific’ module needs to be operational at any time and for how long. Whilst this has proved quite successful, allowing the emergence of unexpected behaviours that rather look ‘intentional’ - although these systems are teleonomic not teleological, (so it is only the ‘observer’ that imputes ‘intention’ to them), we find that significant limitations still exist, e.g. in the need for explicitly ‘designed’ operations. Given that this architecture relies on the environment, but still requires that each module be ‘hand-crafted’, can we go yet a stage further and dispense with that ‘designer’ stage, allowing feedback itself to ‘sculpt’ the robot entirely, i.e. in the same manner as is thought to happen in evolutionary biology? In traditional neo-Darwinian evolution (Futuyma, D.J., 1986) we rely on genetic mutations to generate the variety on which selection then acts. This however is a very long term process, it took billions of years before single celled organisms (e.g. bacteria, which reproduce every 20 minutes or so) achieved multicellularity, and a billion more years were needed before ‘intelligent’ creatures arrived as a reality. It is most unlikely that any of us in the AI community could live long enough to achieve even a basic prototype! Fortunately we can take advantage of some short cuts. One of these acts by using computers, in order to model the multi-generational ‘phylogenetic’ evolution scenario just outlined.
We can, using high speed computers and a technique called ‘genetic algorithms’ (Holland, J., 1992; Lucas, C., 2000a), operate at speeds of hundreds of generations per second - increasing as computers become faster. In this way we are able to ‘evolve’ some structure in a reasonable length of time, but what this happens to contain depends very much upon the constraints we apply to the system. These constraints mimic the ‘selection’ phase of natural evolution, but what should they be? Once again, in order to get what we want, so that we can arrange to ‘select’ for it, we need to know what it is in advance - so how can we avoid designing our ‘fitness function’ appropriately, but so deterministically, and then suffering the problem that evolution stops altogether once the population has ‘converged’ on our fitness optimum? One way around this new problem is to use coevolution.
This means that we let one ‘organism’ act as the fitness function for the other, and vice-versa. By using this sort of technique we can indeed generate improved functionality, for example in deriving efficient ‘sort programs’ where the list of numbers to be ‘sorted’ also evolves in difficulty (Hillis, W.D., 1991). But we have to start somewhere! We must create at least ‘prototypes’ of program and list, before anything at all can happen. So we seem yet again to be forced back to stage one - deliberate design. We can minimise the extent of this to some degree by simply designing-in the ability to adapt to unpredictable environments, as in ‘reinforcement learning’ ( Ackley, D. H. & Littman, M. L., 1992), but the basic ‘intelligence’ to so act must still be ‘crafted’ by our ‘outsider’.
Growing from Scratch
But if we can’t find a way to get over this problem then how can ‘nature’ possibly do so, do we need a ‘God’ as some seem to think? Here we come to the crux of the matter, in considering a process that has been left out of standard neo-Darwinian evolution, that of ‘development’ or ‘ontogeny’. Every human ‘robot’ starts life as a single cell (like our bacteria), this then grows into an embryo and thus eventually is born as a (more or less) functioning human child. But that is not the end of the matter, life experiences then develop, initially, the brain (our neural network wiring) and later (in interactions with others) our mind or ‘personality’, i.e. our behavioural range. It is this latter state we hope to duplicate in AI, so looking at how this process is understood in developmental biology should prove to be a useful indicator as to how we might achieve, artificially, much the same result, and without the need for any form of external ‘intelligent designer’. Unfortunately it isn’t understood very well at all as yet, at least in overall terms! Embryogenesis, as it is called (Bard, J., 1990; Slack, J.M., 1991; Wolpert, L., 1998), consists of three stages. Firstly ‘growth’ (the duplication of cells by asexual or ‘mitotic’ reproduction), secondly ‘differentiation’ (the spliting of cells into different tissue types) and thirdly ‘morphogenesis’ (the creation of form or structure).
The first stage expands possibility space, for every doubling of the number of cells we have a combinatorial explosion, e.g. for just 64 cells we have over 10 89 permutations (64!), and this escalates rapidly during the growth process. But let us look more closely now at the second stage, after all, these permutations only become really interesting if the cells can be distinguished from each other. What is known about this is that there are many genetic regulatory networks involved, each ‘cell type’ (and there are hundreds) activates a different set of genes. These regulatory ‘controls’ do not however operate in a linear hierarchy, 1:N, in the way that we often regard human control networks, nor do they have fixed functions, independently maintained, as we often consider in AI ‘subroutines’. What happens is that they are arranged into a web, in N:M fashion, where each ‘gene’ inter-links with many others, switching them on and off and being controlled in a like manner (Lucas, C., 2004b). This is a ‘circular feedback’ form of causality, called ‘polygeny’ and ‘pleiotropy’, which actually operates on several different levels. The same processes of activations and inhibitions also take place between cells (the third stage of embryogenesis), and this includes the inter-neuron connections in our mind. We can even, if we wish, go beyond the mind as an entity in itself and venture out into the wider world, and we shall see exactly the same phenomenon, in society and in ecology both, i.e. a ‘systems’ viewpoint of some type is applicable in all areas of our world.
Self-Organization Arrives
What is common then to all these processes and levels? Well, it is the idea of ‘self-organization’ which is our focus in this article, which we can define like this (Lucas, C., 2004d):
The essence of self-organization is that system structure often appears without explicit pressure or involvement from outside the system. In other words, the constraints on form (i.e. organization) of interest to us are internal to the system, resulting from the interactions among the components and usually independent of the physical nature of those components. The organization can evolve in either time or space, maintain a stable form or show transient phenomena. General resource flows within self-organized systems are expected (dissipation), although not critical to the concept itself. The field of self-organization seeks general rules about the growth and evolution of systemic structure, the forms it might take, and finally methods that predict the future organization that will result from changes made to the underlying components. The results are expected to be applicable to all other systems exhibiting similar network characteristics.
In other words, we now have a method of generating demonstrable structure for ‘free’ (Heylighen, F., 1999), of getting over (maybe) our primary ‘design’ problem. To see just how significant this is, let us add a few numbers. Suppose we have 10,000 randomly connected, 2 input, logic gates (what is called a ‘random Boolean network’ of size N - Lucas, C., 2002b), in other words the number of different possibilities is 2 10,000 - a staggering number. On average (with considerable variance) we would expect any area of the self-organized system to only visit (cycle through) 100 (square root of N) different dynamical states - this is less than 2 7, and there will be (again, with considerable variance) only about 100 disjoint areas of activity, 2 7 again (root N). So the ratio of initial ‘disorder’ to final ‘order’ is a massive 2 9986 or so ! This is not quite such a ‘magic’ solution as we may wish however, we still must have ‘something’ initially to work with (the gates here), but this proves to be our second ‘short-cut’. If the ‘lower levels’ also emerged (as we think) in this way, then we can ‘cut to the chase’ as it were, and ignore how they actually got there. We thus can start off with ‘parts’ of appropriate types for our AI purposes, called ‘agents’, and let a collection of these coevolve and self-organize, bottom-up, to meet our needs. All we have to do then is to sit back and watch it happen... Studies of the dynamics of such scenarios (Kauffman, S., 1995) show that three general results are possible. In the first, the agents are insufficiently connected (too cool), they don’t interact much at all, so the system quickly settles into a fixed state, we have convergence to a ‘static’ result (akin to the traditional single analytical ‘solution’ in science). In the second the agents are highly connected (too hot) and each affects many others constantly, here the system cannot settle, it is always ‘perturbed’ (Lucas, C., 2000b) and exhibits a ‘chaotic’ behaviour (those ‘insoluble’ systems usually ignored in science). In the third state, which I call ‘Type 4 Complexity’ (Lucas, C., 1999), we have a (just right) behaviour that modularises the system, with some sets of agents proving to be static, some chaotic, and some dynamic - many of which will swap places over time as the system evolves between the possible (semi-stable or multistable) ‘attractors’ (Lucas, C., 2002a).
In this scenario we find that the maximum ‘fitness’ can be achieved, the best overall performance is within reach. For larger systems, the dynamics will achieve a ‘fractal’ or ‘power law’ spread of properties, called ‘Self-Organized Criticality’ or ‘Edge-of-Chaos’, (Bak, P., 1996; Lucas, C., 2000b), which can give a somewhat emergent multi-layered or hierarchical structure, with inherent cooperative behaviour between the parts becoming apparent (Ünsal, C., 1993). This idea of appropriate ‘connectivity’ is proving to be highly important in many areas of our world, from the social ones related to ‘anarchy’, ‘democracy’ and ‘totalitarianism’, via medical ones related to ‘epidemics’, through to ecological ones related to ‘diversity’ and freedom of combination, not to mention the physics or mathematical ones related to ‘spin glasses’ or ‘percolation’. By arranging connectivity suitably we can enable our important self-organizational processes.
This is the ‘communications’ aspect of agent interaction, but two other aspects also need to be included here if we are to achieve success in our self-organizational scenarios. The first is appropriate size, relating to decentralisation. Systems must be small enough to be self-contained - if they are too big then the inertia of ‘bureaucracy’ inhibits the recognition of any ‘improvement’; but they should not be too small either - else they will have insufficient ‘variety’ with which to make improvements. The other aspect is ‘stress’, a desire or a need for improvement. Again if this proves to be too high the system will disintegrate, we will have rapid breakdown; but if the stress is too low then the ‘status-quo’ cannot be overcome and a static state will persist. Given that these ‘middle-way’ conditions are met, then self-organization should occur and the system will generate our required ‘novelty’ or emergence.
A Competitive Problem
Although this explanation sounds very glib and easy, in practice there are a number of problems, for example in the (very visible) social and environmental destructiveness that we can see around us, resulting from the unfettered individualism driving the self-organization (‘invisible hand’) of the (over-stressed) ‘free market’. Experiments using Multi-Agent Systems (MAS), which operate using these ideas of self-organization, have also not so far achieved those higher levels of structure that we so desire and expect (seen in nature in the progression atoms- molecules- cells- organisms- societies- ecologies), and which are commonly to be found in the behaviour of ‘swarms’, for example, in insect societies ( Bonabeau, E. et al., 2002), where ‘stigmergy’ (environmentally mediated communication) also has an important effect (Holland, O. & Melhuish, C., 1999) .
These current failures may well be because of the assumptions embedded within the agent structures typically used. In so many current systems, there is an inherent ‘competitiveness’ - echoing the belief behind the phrase ‘survival of the fittest’ often employed by Darwinists (and capitalists). Yet let us consider cellular development once more, what would be the effects of such ‘competition’? The answer seems pretty clear, it is the same as what happens when we suffer from ‘cancer‘ - the competition from ‘rogue’ cells eventually destroys the host. Thus it is not ‘competition’ that we need, but ‘cooperation’. We need to find a way for the agents to ‘work together’, since only in this way can organisms (and/or societies) function and persist at ‘higher levels’. The principle we are looking for, which we wish to employ within our self-organizing systems, is called ‘synergy’ (Corning, P., 1995; Lucas, C., 2004c). Here, when two or more agents come together, a new ‘functionality’ arises, they gain combined powers greater than the sum of their separate powers, often illustrated with the phrase the whole is greater than the sum of the parts. But how can this possibly work? In essence, by a form of combinatorial trial and error - in which, in the processes of interaction, this new higher level functionality arises. Thus there is initially a diversity and an ongoing novelty, as seen in the pairwise encounters of the heterogeneous agents, but in some way these agents then ‘associate’. This, like the sexual crossover experienced in evolution, allows new ‘building blocks’ to arise, new combinations of functions which may, perhaps, operate in an entirely new way - we have potential ‘emergence’ (Lucas, C., 2004b). In the operation of the typical MAS the agents interact and learn (at least partly) in a random fashion, and their individual behaviours change, but they do so not only as a result of their own experiences - we find instead that the ‘higher level’ places new constraints upon them (Epstein, J.M. and Axtell, R., 1996). These constraints, called ‘downward causation’ (Campbell, D.T., 1974; Lucas, C., 2004b) add new values to the system, new environmental relevances at a ‘group’ level that imply selective forces beyond those of the individual. Although such ideas have been resisted within biology for some decades, only recently making a comeback (Wilson, D.S. & Sober, E., 1994), they do prove to be valid from both computing and complexity science perspectives, e.g. (Sloman, A. & Chrisley, R., 2003). For this to prove useful however there must be a possibility of a dynamic from the agents to the ‘end result’, a way of searching current ‘state space’ (Lucas, C., 2002a), and expanding it in ways that enables such new functionality. Yet this aspect of emergence is very much under researched so far, we have very little idea as yet as to how we can arrange systems such that anything predictable will emerge, let alone to achieve what we, ideally, would wish to see. This is perhaps the greatest challenge to be met in the future by the complexity science community. But given this limitation, can the ideas we have outlined contribute already in any way to current robotic research ? We shall see that they can and they do.
Enter the Robot
An implied embryogenesis perspective provides some, highly scaleable, advantages for robot designers (Bentley, P.J. & Kumar, S., 1999). These include ‘adaptability’, the ability to respond to context (Quick, T. et al., 1999); ‘compactness’, the ability to code large structures in an efficient form; and ‘repetitiveness’, the ability to reuse the same structures or subroutines for many different functions. By using these techniques, in for example the evolutionary design of neural networks ( Astor, J.C & Adami, C., 2000) , we can evolve functional robot controllers (Jacobi, N., 1995), which potentially can interface with humans (Kanada, Y. & Hirokawa, M., 1994). Self-organization is also a useful biological technique which can be used for evolving robotic functionality (Nolfi, F. & Floreano, D., 2000; Kim, D.H., 2004) , thus by combining the two perspectives (low-level agent development and higher level agent interactions) we may, advantageously, enable an ‘embodied’ form of ‘autopoietic’ (Lucas, C., 2004a) emergence, a coevolution of situation and actor. Note that we have two opposing drives here, the first (embryogenesis) expands state space, it add new possibilities, new options or combinations to the mix, the second (self-organization) reduces this diversity, it selects from those many options only those possibilities than can persist, the functionally stable states of the system. It moves from the system’s starting points in state space to its ‘attractors’. But these should not be viewed in isolation, they are only stable in terms of the current environment, if the context changes then that stability can be lost and another, alternative, stable state must then be found. This is, in fact, what we mean by learning or ‘epigenesis’, the move from one stable state in a certain context to another stable state in a different context. If our robot cannot do this, if it fails to ‘adapt’, becoming what we term ‘fragile’, then it has insufficient options (‘requisite variety’ in cybernetic terminology) to cope with the diversity of its environmental perturbations. Designing robots that can overcome this tendency to be highly domain restricted has been a major headache in AI history, so how can our new perspective help? If we are to ‘grow’ some form of robot from scratch then four aspects are necessary. Firstly we must have a part (or a set of different parts) which can increase in number, secondly those parts must be able to associate (communicate and/or stick together) in some way such that they can form aggregates (some equivalent to ‘cell-adhesion’ or ‘morphoregulatory’ molecules - Edelman, G., 1992), thirdly they must have the freedom to self-organize, (i.e. to change their configurations and communications dynamically), and finally we need to allow for environmental influences to be able to trigger these reorganizations (allowing adaptability). To see the latter two aspects in a different light, self-organization restricts (‘canalizes’) the possibilities open to the system, it is a form of internal selection. The environment puts stress or bias on the system to achieve a viable function, causing it to escape poor attractors and flip to better ones, it is a form of external selection (if our system can’t so adapt it simply dies). But as we add new units to the mix, as we ‘grow’ the self-organizing system (Fritzke, B,. 1996), perhaps creating a 3D ‘morphology’ e.g. ( Eggenberger, P., 1997), then we both add to and reconfigure its attractors, so that in this way we can increase the ‘requisite variety’ until our system can, in fact, cope with the target environment. This is similar to the way in which we make additional synaptic neuronal connections with learning, we increase the complexity of the system by creating additional ‘concepts’ or ideas, new options or associations. Incorporating these four aspects into a real robot system is however a very demanding task, and not one that has yet been attempted.
Firstly we cannot grow artificial parts, they do not ‘reproduce’ in any sense. The best we can do is either to ‘simulate’ such systems (perhaps eventually implementing the end result, e.g. Bentley, 2004), or to have our robot (somehow) pick up and incorporate extra parts that happen to be ‘lying around’ in the environment. Secondly it is unclear how we should have the parts interact in a way suitable for stimulating development, such that it can possibly start to self-organize itself. Thirdly we have the problem of how to allow the environment to change the configuration, to ‘disturb’ the robot in some way, in such a manner as to force it to re-design itself. Even if these three major obstacles are overcome, then we still do not know how to use our parts to self-assemble ‘critters’ with specific functions - but of course we still don’t know how nature does that either (Raff, R.A., 1996). This relates to understanding how the three processes (phylogeny, ontogeny and epigenesis) interact, but once we can do this then we have the potential to build what have been called ‘POEtic machines’ (Teuscher, C., 2001). For the future, perhaps all we can say, is that we live in interesting times...
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