Ill robots might get a temperature too

Just spent 4 days at the beautiful Schloss Dagstuhl in SW Germany attending a seminar on Artificial Immune Systems. The Dagstuhl is a remarkable concept – a place dedicated to residential retreats on advanced topics in computer science. Everything you need is there to discuss, think and learn. And learn is what I just did – to the extent that by lunchtime today when the seminar closed I felt like the small boy who asks to be excused from class because “miss, my brain is full”.

Knowing more or less nothing about artificial immune systems it was, for me like sitting in class, except that my teachers are world experts in the subject. A real privilege. So, what are artificial immune systems? They are essentially computer systems inspired by and modelled on biological immune systems. AISs are, I learned, both engineering systems for detecting and perhaps repairing and recovering from faults in artificial systems (in effect system maintenance), and scientific systems for modelling and/or visualising natural immune systems.

I learned that real immune systems are not just one system but several complex and inter-related systems, the biology of which is not fully understood. Thus, interestingly, AISs are modelled on (and models of) our best understanding so far of real immune systems. This of course means that biologists almost certainly have something to gain from engaging with the AIS community. (There are interesting parallels here with my experience of biologists working with roboticsts in Swarm Intelligence.)

The first thing I learned was about the lines of defence to external attack on bodies. The first is physical: the skin. If something gets past this then bodies apply a brute force approach by, for instance raising the temperature. If that doesn’t work then more complex mechanisms in the innate immune system kick-in: white blood cells that attempt to ‘eat’ the invaders. But more sophisticated pathogens require a response from the last line of defence: the adaptive immune system. Here the immune system ‘learns’ how to neutralise a new pathogen with a process called clonal selection. I was astonished to learn that clonal selection actually ‘evolves’ a response. Amazing – embodied evolution going on super-fast inside your body within the adaptive immune system, taking just a couple of days to complete. Now as a roboticist I’m very interested in embodied evolution – and by coincidence I attended a workhop on that very subject just a month ago. But I’d always assumed that embodied evolution was biologically implausible – an engineering trick if you like.  But no – there it is going on inside adaptive immune systems. (As an aside, it appears that we don’t understand the processes that prompted the evolution of adaptive immune systems some 400 million years ago – in jawed vertebrates).

Of course while listening to this fascinating stuff I was all the while wondering what this might mean for robotics. For instance what hazards would require the equivalent of an innate immune response in robots, and which would need an adaptive response. And what exactly is the robot equivalent of an ‘infection’. Would a robot, for instance, get a temperature if it was fighting an infection. Quite possibly yes – the additional computation needed for the robot to figure out how to counter the hazard might indeed need more energy – so the robot would have to slow down its motors to direct its battery power instead to its computer. Sounds familiar doesn’t it: slowing down and getting a temperature!

Swarm robots with faults is something I’ve been worrying about for awhile and, based on the work I blogged about here, at the Dagstuhl I presented my hunch that – while swarm of 100 robots might work ok – swarms of 100,000 robots definitely wouldn’t without something very much like an immune system. That led to some very interesting discussions about the feasibility of co-evolving swarm function and swarm immunity. And, given that we think we’re beginning to understand how to embed and embody evolution across a swarm of robots, this is all beginning to look surprisingly feasible.

Why Slow Science may well be A Very Good Thing

A few weeks ago I spent a very enjoyable Saturday at the Northern Arts and Science Network annual conference Dialogues, in Leeds. The morning sessions including two outstanding keynote talks. The first from Julian Kiverstein on synthetic synaesthesia and the second from David James on technology enhanced sports. Significant food for thought in both talks. Then Jenny Tennant Jackson and I ran an afternoon workshop on the Artificial Culture project (aided and abetted by 8 e-puck robots) which generated lots of questions and interest.

But apart from singing the praises of NASN and the conference I want to reflect here on something that emerged from the panel discussion at the end of the conference. There was quite a bit of debate around the question of open research (in both science and the arts) and public engagement. In recent years I've become a strong advocate of a unified open science + public engagement approach. In other words doing research transparently - ideally using an open notebook approach so that the whole of the process as well as the experimental outcomes are open to all - combined with proactive public engagement in (hopefully) a virtuous circle*.

So there I was pontificating about the merits of this approach in the panel discussion at NASN when someone asked rather pointedly "but isn't that all going to slow down the process of advancing science?" Without thinking I retorted "Good! If the cost of openness is slowing down science then that has to be a price worth paying." The questioner was clearly somewhat taken aback and to you sir, if you should read this blog, I offer sincere apologies for the abruptness of my reply. In fact I owe you not only apologies but thanks, for that exchange has really got me thinking about Slow Science.

So, having reflected a little, here's why I think slowing down science might not be as crazy as it sounds.

First the ethical dimension. Science or engineering research that is worth doing, i.e. is important and has value, has - by definition - an ethical dimension. The ethical and societal impact of science and engineering research needs to be acknowledged and understood by researchers themselves then widely and transparently debated, and not left to bad science journalism, science denialism or corporate interests. This takes time.

Next, unintended consequences. High impact research always has implications, and the larger the impact, the greater the potential for unintended consequences (no matter how well intentioned the work). Of course negative unintended consequences (scientific, economic, philosophical) almost always end up becoming a problem for society - so they too should be properly considered and discussed during a project's lifetime.

Finally the open science, public engagement dimension. I would argue that the time and effort costs of building open science and public engagement into research projects will reap manifold dividends in the long run. First take the open science aspect, openness - while it can take some courage to actually do - can surely only bring long term benefits in increased trust (in the work of the project, and in science in general). Second, running an integrated open science - public engagement approach alongside the research brings direct educational benefit to the next generation. And the additional real cost (in time and effort) has to be much less than it would be for an isolated project seeking the same educational outcomes.

Critics will of course argue that Slow Science would be uncompetitive. In a limited sense they would be right, but it seems to me important not to confuse commercialisation of spin out products with the much longer time span of research, nor to allow the tail of exploitation to wag the dog of research. Big science that takes decades can still spin out lots of wealth creating stuff along the way. Another criticism of Slow Science is to do with pressing problems that desperately need solutions. This is harder to counter but - perhaps - the unintended consequences argument might hold sway.

Slow Science: a Good Thing, or not?

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*science communicator and PhD student Ann Grand is researching exactly this subject and has already published several papers on it.

Telling all on I'm a Scientist

In future if anyone wants to know what I think - about almost anything scientific and quite alot else - all I have to do is point them to my profile and my collected answers on I'm a Scientist get me out of here. It's been a week now since IAS concluded and the winners announced and I've had time to collect my thoughts, catch up on the day job, and reflect on taking part in this most excellent event.

I'm a Scientist get me out of here is aptly named. By Thursday on the second week I was - on balance - more relieved than disappointed to be evicted from the virtual jungle clearing, called the Chlorine Zone, that I'd been sharing with four other scientists. (Beyond the eviction thing the analogy with I'm a Celebrity breaks down. We five were not required to undertake challenges designed to freak-out the squeamish nor rewarded with discomfort reducing morsels.)

No. I'm a Scientist is an altogether more civilised affair. It's a direct engagement with school children; meet-the-scientist on-line in which school children can ask the scientists questions on more or less anything they like. There are two types of engagement, chat and ask. The live chat sessions are booked by teachers and scheduled during school science lessons - a bit like having a panel of scientists sitting at the front of the classroom answering questions, except it's on-line. Ask allows the children to submit their questions through the web page for the scientists to answer in their own time. Both types of engagement are moderated by the good people who run I'm a Scientist.

Why then - if I'm a Scientist is so wonderful (which it is) - was I relieved to be evicted? Well, it's because after nearly 2 weeks the questions just keep coming and trying to keep up (especially given that we all have day jobs) became, if I'm completely honest, something of a test of endurance. Not counting the live chat school sessions I answered about 175 questions altogether. Other I'm a Scientist scientists who read this will scoff and say "pah, only 175!". And they'd be right - Sarah Thomas in my zone answered over 300 questions, and the awesome David Pyle in the potassium zone around 600! But even my paltry 175 questions took I reckon about 30 hours to answer, at an average 10 minutes per question (which is going fast).

But I'm not going to whinge here about my inability to keep up (although I do strongly advise future I'm a Scientists to set aside plenty of question answering time). I really want to reflect on the questions themselves. Firstly I was slightly surprised there were so few on my specialist subject of robotics. Only 22 out of the 175. But they were good ones! Here are some of my favourites:

• Do you think that us humans could ever start turning like robots and get all high tech like robots?
• Do you believe truly that robots can be just like a human? Do they have all of the 5 senses?
• Would it be possible to train/build a robot that is able to fight in wars?
• Will your research help understand how our brain works?

Some of these will form the basis of future blog posts. But it was the general science questions that were the most interesting, for instance:

• Do you think the idea of a graviton is stupid? 
• How likely is it that there are other life forms like us?
• Atoms and particles act in probabilistic ways and our brain is made up of atoms and particles, so is there such thing as free will?

Brilliant - it was a kind of science soap box! I got to pontificate on life on Mars, the end of the world and human extinction, global warming, nuclear power, dreams, light years, my favourite animal, my favourite car, string theory, the Higgs Boson and dark matter. But the non-science questions make you stop and think - hmm how much do I want to reveal about what I think about antidisestablishmentarianism, my religous beliefs, resurrection or the meaning of life..?

By far the biggest category of questions was about doing science: why and how you do science, what's the best thing about being a scientist, what you think you have achieved, or will achieve and so on (and quite a few on what you will do with the prize money if you win). These are great questions because they allow you to explode some myths about science: for instance that you have to be super smart to do science, or that one scientist can change the world on their own. I was especially flattered by

• You seem like the elder of the Science-Group - do you think knowledge come with experience?

If you're thinking of putting yourself forward for I'm a Scientist I would say yes go for it. It's hugely good fun and massively worthwhile. But (1) set aside plenty of time, (2) be prepared to answer questions on more or less anything and (3) be honest about yourself and what you really think about stuff.

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Here are some great blog posts from other March 2011 I'm a Scientists:
Suzie Sheehy's Reflections on I'm a Scientist
David Pyle's I'm a Scientist: 600 questions later
Eoin Lettice's I'm a Scientist and I'm out of here

Dilemmas of an ethical consumer

I have a dilemma and it is this. I'm torn between lusting after an iPad 2 and serious worries over the ethics of its manufacture.

There's no doubt that the iPad is a remarkable device (Jobs' hyperbole about magical and revolutionary is quite unnecessary). Several academic friends have told me that the iPad and one application in particular - called iAnnotate - has changed their working lives. Having seen them demonstrate iAnnotate there's no doubt it's the academic's killer iPad app. You see, something we have to do all the time is read, review and edit papers, book chapters, grant applications and working documents. For me that normally means printing a paper out, writing all over it, then either tediously scanning the marked up pages - uploading them to Google docs - then emailing the link, or constructing a large email with a list of all my changes and comments. What my friends showed me was them reviewing a paper on the iPad, writing all over it with a stylus, then just emailing back the marked up document. Amazing - this could save me hours every week.

But here's the problem. The iPad may well be a marvel of design and technology but - like most high tech stuff these days - it's profoundly unsustainable and it's manufacture is ethically questionable. Now to be fair to Apple, this is not a problem that's unique to them - and I'm prepared to believe that Apple does genuinely care about the conditions under which it's products are manufactured and is doing all it can to pressure it's subcontractors to provide the best working conditions for their employees. But the problem is systemic - the only reason that we can buy an iPad, or laptop, or flat screen TV, or any number of consumer electronics products for a few hundred pounds is that they're manufactured in developing countries where labour is cheap and working conditions are a million miles from what we would regard as acceptable. And I'm not even going to start here about the sustainability of those products - in terms of the true energy costs, and costs to the environment, of their manufacture during incredibly complex supply chains, or the environmental costs of their disposal after we've finished with them.

This may sound odd given that I'm a professional electronics engineer and elder-nerd. But I'm a late adopter of new technology. Always have been. (My excuse is that I was an early adopter of the transistor.) I also keep stuff for a very long time. My Hi-Fi system is 25 years old and is working just fine. My car is now 6 years old and I fully expect to run it for another 10 years - a modern well-built and maintained car can easily last for 250,000 miles. The most recent high tech thing I bought was a new electric piano. It replaced my old one, bought in 1983, which had become unplayable because the mechanics of the keys had worn out and I fully expect to keep my beautiful new Roland piano for 25 years. My MacBook pro (yes I do like Apple stuff) is now 5 years old and works just fine - not bad for something that's probably had 10,000 hours use. In short I aim to practice what's sometimes called Bangernomics - except I try and apply the philosophy to everything, not just cars. (I'm not exactly a model consumer.)

Maybe that's part of the answer to my dilemma - get an iPad and run it for 20 years..? But even applying Bangernomics still won't salve my conscience when it comes to the ethics or sustainability of its manufacture. So, what am I to do?

Making sense of robots: the hermeneutic challenge

One of the challenges of the artificial culture project that we knew we would face from the start is that of making sense of the free running experiments in the lab. One of the project investigators - philosopher Robin Durie - called this the hermeneutic challenge. In the project proposal Robin wrote:

what means will we be able to develop by which we can identify/recognise meaningful/cultural behaviour [in the robots]; and, then, what means might we go on to develop for interpreting or understanding this behaviour and/or its significance?

Now, more than 3 years on, we come face to face with that question. Let me clarify: we are not - or at least not yet - claiming to have identified or recognised emerging robot culture. We do, however, more modestly claim to have demonstrated new behavioural patterns (memes) that emerge and - for awhile at least - are dominant. It's an open-ended evolutionary process in which the dominant 'species' of memes come and go. Maybe these clusters of closely related memes could be labelled behavioural traditions?

Leaving that speculation aside, a more pressing problem in recent months has been to try and understand how and why certain behavioural patterns emerge at all. Let me explain. We typically seed each robot with a behavioural pattern; it is literally a sequence of movements. Think of it as a dance. But we choose these initial dances arbitrarily - movements that describe a square or triangle for instance - without any regard whatsoever for whether these movement sequences are easy or hard for the robots to imitate.

Not surprisingly then, the initial dances quickly mutate to different patterns, sometimes more complex and sometimes less. But what is it about the robot's physical shape, its sensorium, and the process of estimation inherent in imitation that gives rise to these mutations? Let me explain why this is important. Our robots and you, dear reader, have one thing in common: you both have bodies. And bodies bring limitations: firstly because you body doesn't allow you to make any movement imaginable - only ones that your shape, structure and muscles allow, and secondly because if you try to watch and imitate someone else's movements you have to guess some of what they're doing (because you don't have a perfect 360 degree view of them). That's why your imitated copy of someone else's behaviour is always a bit different. Exactly the same limitations give rise to variation in imitated behaviours in the robots.

Now it may seem a relatively trivial matter to watch the robots imitate each other and then figure out how the mutations in successive copies (and copies of copies) are determined by the robots' shape, sensors and programming. But it's not, and we find ourselves having to devise new ways of visualising the experimental data in order to make sense of what's going on. The picture below is one such visualisation; it's actually a family tree of memes, with parent memes at the top and child memes (i.e. copies) shown branching below parents.

Unlike a human family tree each child meme has only one parent. In this 'memeogram' there are two memes at the start, numbered 1 and 2. 1 is a triangle movement pattern, and 2 is a square movement pattern. In this experiment there are 4 robots, and it's easy to see here that the triangle meme dominates - it and its descendants are seen much more often.

The diagram also shows which child-memes are high quality copies of their parents - these are shown in brown with bold arrows connecting them to their parent-memes. This allows us to easily see clusters of similar memes, for instance in the bottom-left there are 7 closely related and very similar memes (numbered 36, 37, 46, 49, 50, 51 and 55). Does this cluster represent a dominant 'species' of memes?

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Also posted on the Artificial Culture project blog.

A sick robot dog called Max

A friend has asked me to check out her Aibo robot dog, called Max. Here he is:

Cute eh?

He's an early ERS-210 model. Charges up ok, but there's no response on switching-on. Hmm. I suspect the programme memory stick might have become corrupted. This will need a deeper examination...

FIRA 2012 Robot World Cup to be hosted by the Bristol Robotics Lab

We're all very excited because FIRA (the Federation of International Robot soccer Association), which runs an annual competition for robot soccer (and other robot sports), has awarded the 2012 event to the Bristol Robotics Lab. The 2010 event was held in Bangalore, India: check here for the web pages with 2010 results and some terrific videos. This year FIRA 2011 will be in Kaohsiung, Taiwan.

FIRA 2012 will run from 20 - 25 August 2012, just a week or so after London 2012. Alongside FIRA 2012 will be two robotics conferences: the FIRA Congress and TAROS 2012 (Towards Autonomous Robotic Systems). Here is the (under development) FIRA-TAROS 2012 web site. Here is the joint University of Bristol, UWE press release announcing the event.

The FIRA robot world cup games currently fall into 7 categories and each category is defined by the type of robot and, typically, has its own set of rules. The first six categories are all real physical robots, the 7th - SimuroSot - is all in simulation. Here's a brief summary of the 6 real robot categories with links to the full descriptions and rules on the FIRA web pages.

• HuroSot is the main category for bipedal (walking and running) humanoid robots. It is also the most comprehensive category - in addition to soccer the category includes competitions for basketball, wall climbing, weight lifting and marathon running. HuroSot robots can be up to 130cm in height, and weigh up to 30kg. We will be entering a Bristol team for HuroSot. Here are some nice videos of HuroSot competitions in 2009.
• Amiresot is a simple one-a-side soccer game for the small Amire wheeled robot, which must be fully autonomous with its own vision system. AmireSot robots play with a yellow tennis ball.
• MiroSot is the Micro Robot soccer game for wheeled robots. It's a three-a-side game (one player can be a goalkeeper), in which an external vision system tracks the position of robots - and the ball - and an external computer system computes and relays moves to the robots. Robots cannot be larger than 7.5cm x 7.5cm x 7.5cm and they play with an orange golf ball. Here is a page with a video of a 2009 MiroSot game.
• NaroSot is similar to MiroSot but with smaller wheeled robots (4cm x 4cm x 5.5cm) and is a five-a-side game. NaroSot robots play with an orange ping-pong ball.
• AndroSot is a three-a-side game for fully autonomous 'android' robots between 30 and 60cm in height. Here is a video of a 2009 AndroSot game. 
• RoboSot is a game for larger wheeled robots (20cm x 20cm x any height). It's a three-a-side game and the robots must use on-board vision, although computation may be off-board. RoboSot robots play with a yellow/green tennis ball.

Here are some of the robots entered in past competitions (from the FIRA web pages):

HuroSot

MiroSot

NaroSot

RoboSot

On Twitter and Machiavellian Intelligence

Four short months and 135 tweets ago I wrote about joining Twitter. Slightly reluctant, confused by how it worked and - if I'm completely honest - a bit sniffy about whether I could be bothered with it at all.

But just a week ago I stunned myself by realising that Twitter is now the first thing I check in the morning. Not email. After the best part of 15 years of first ritually checking my email Twitter has knocked email off the top spot(1). So what happened? What is it about Twitter that is so compelling, so addictive? Why do I love Twitter?

Actually that was just the first surprise. The second was to realise I was so pleased when my number of followers reached 50, then 60 - and last week 70.

But the thing that shocked me rigid a week ago was this. I found myself wondering how I might contrive person X (who I admired) to notice me and become my follower. What the hell was I thinking! Who's in control here - me or Twitter; do I have obsessive compulsive twitter syndrome? Do I need help - maybe go cold turkey for awhile?

But then I started thinking about it and realised that there is a very ancient instinct at work here, and Twitter is just tickling that instinct in me. Perfectly. I'm talking about Machiavellian intelligence. The kind of social intelligence that is present to some degree in all primates and well developed in Chimpanzee and monkeys such as Rhesus Macaques. So what is this kind of social intelligence? Well, if you find yourself thinking: I'm going to make friends with him and pretend I like him, but not because I want to be his friend. Oh no: he has a friend that I really want to be friends with and - through this deception - I might achieve that goal. Then you are engaged in the social politics of Machiavellian intelligence. For anyone interested in intelligence, the evolution of human intelligence, or indeed AI, Machiavellian intelligence is very interesting because it requires Theory of Mind. It was probably already well developed in the most recent common ancestor of humans and chimpanzee, around 6 million years ago.

I don't know whether it was intentional, but the very smart people who created Twitter have somehow built an ecosystem perfectly suited for this kind of game. The basic ingredients are these: firstly everyone has followers and people they follow (following). The fact that you can easily see the number of followers and following for those you follow, or who follow you, means that very quickly you establish exactly where you are in the Twitter social pecking order. These numbers mean alot to us. The alpha-tweeters are those with huge numbers of followers. They are, in the terminology of memetics, meme-founts - leaders of fashion. But even for those of us with modest circles of followers and following, the balance of numbers is significant. Our Machiavellian instinct tells us that those with a greater number of followers than following are, on balance, leaders whereas those whose following outnumbers their followers are, on balance, followers and therefore of lower Twitter status. Please understand I'm absolutely not saying they are less worthy individuals, only that this is what our Machiavellian instinct tells us in the game of Twitter.

The second ingredient that is, I think, significant is the fact that you can easily see which followers or following you have in common with someone. So it is not just a matter of numbers, it's personal. Among those you follow, and those who follow you, you really can work out very quickly who is connected to who - and the connections have social structure. If I and someone else follow each other, then we are - in a sense - equal. If, on the other hand, I see that I'm following someone else, but they don't follow me, then my Machiavellian instinct places them higher up the Twitter social scale than me. Again this may not correlate at all to real-life standing. The point I'm making is that we can't help making these Machiavellian inferences - and Twitter makes it so easy.

This brings me to the third and most brilliant ingredient: Re-tweeting. The politics of re-tweeting are fascinating and complex. Having one of your tweets re-tweeted is the equivalent of being stroked, and we love being stroked. I certainly experience a quantum of happiness(2) when one of my tweets is re-tweeted, and I'm even happier if it's re-tweeted several times. Conversely, I'm disappointed if a tweet that I thought was especially witty, insightful or apposite to current events fails to be re-tweeted. Indeed it appears to be good manners to thank those who have RT'd a tweet - which says alot of how much we value RTs. And of course to be re-tweeted by a Twitter celebrity is a precious honour, the equivalent of a favour by one of the princesses of the Twitter court.

So Twitter is powerful stuff. It's not just a micro-blogging site, it is a quite remarkable place in which we can play out to the full our ancient instinct for Machiavellian social politics.

And of course Twitter has proven itself to be a marvellous vehicle for grass-roots political activism. Is that something to do with Machiavellian intelligence too?

So now I don't feel quite so bad about my new-found Twitter addiction.

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(1) Apart from a short spell of Guardian Soulmates 3 years ago:))
(2) I propose a new unit for a quantum of happiness: the RT (re-tweet).

How Intelligent are Intelligent Robots?

When giving talks about intelligent robots I've often been faced with the question "how intelligent is your robot?" with a tone of voice that suggests "...and should we be alarmed" It's a good question but one that is extremely difficult - if not impossible - to answer properly. I usually end up giving the rather feeble answer "not very", and I might well add "perhaps about as intelligent as a lobster" (or some other species that my audience will regard as reassuringly not very smart). I'm always left with an uneasy sense that I (and robotics in general) ought to be able to give an answer to this perfectly reasonable question. (Sooner or later I'm going to get caught out when someone follows up with "and exactly how intelligent is a lobster?")

Given that the study of Artificial Intelligence is over 60 years old, and that of embodied AI (i.e. intelligent robotics) not much younger, the fact that roboticists can't properly answer the question "how intelligent are intelligent robots" is, to say the least, embarrassing. It is I believe a problem that needs some serious attention.

Let's look at the question again. There is an implied abbreviation here: what my interlocutor means is: how intelligent are intelligent robots when compared with animals and humans? What's more we all assume a kind of 'scale' of intelligence - with humans (decidedly) at the top and, furthermore, a sense that a crocodile is smarter than a lobster, and a cat smarter than a crocodile. Where, then, would we place a robot vacuum cleaner, for instance, on this scale of animal intelligence?

Ok. To answer the question we clearly need to find a single measure, or test, for intelligence that is general enough it can be applied to robots, animals or humans. It needs to have a single scale broad enough to accommodate human intelligence and simple animals. This metric - let's call it GIQ for General (non-species-specific) Intelligence Quotient - would need to be extensible downwards - to accommodate single celled organisms (or plants for that matter) and of course robots because they're not very smart. Thinking ahead it should also be extensible upwards for super-human AI (which we keep being told is only a few decades away). Does such a measure exist already? No, I don't think it does, but I did come across this news posting on physorg.com a few days ago with the promising opening line How do you use a scientific method to measure the intelligence of a human being, an animal, a machine or an extra-terrestrial? It refers to a paper titled Measuring Universal Intelligence: Towards an Anytime Intelligence Test. I haven't been able to read the paper (it is behind a paywall) but - even from the abstract - it's pretty clear the problem isn't solved. In any event I'm doubtful because the news writeup talks of "interactive exercises in settings with a difficulty level estimated by calculating the so-called Kolmogorov complexity", which suggests a test that the agent being tested has to engage in. Well that's not going to work if you're testing the intelligence of a spider is it?

So let's set aside the problem of comparing the intelligence of robots with animals (or ET) for a moment. Are there existing non-species specific intelligence measures? This interesting essay by Jonathan Ball: The question of animal intelligence outlines several existing measures based on neural physiology. In summary they include:

• Encephalization Quotient (EQ): which measures whether the brain of a given species is bigger or smaller than would be expected, compared with that of other animals its size (winner: Humans)
• Cortical Folding: a measure based on the degree of cortical folding (winner: Dolphins)
• Connectivity: a measure based on comparing the average number of connections per neuron (winner: Humans)

Interestingly, if we take the connectivity measure - which Jonathan Ball suggests offers the greatest degree of correlation with intelligence - then if our robot is controlled by an artificial neural network we might actually have a common basis for comparison of human and robot intelligence.

So, even if none of them are entirely satisfactory it's clear that there has been a great deal of work on measures of animal intelligence. What about the field of robotics - are there intelligence metrics for comparing one robot with another (say a vacuum cleaning robot with a toy robot dog)? As far as I'm aware the answer is a resounding no. (Of course the same is not true in the field of AI where passing the Turing Test has become the iconic - if controversial - holy grail.)

But all of this presupposes, firstly, that we can agree on what we mean by 'intelligence' - which we do not. And secondly, that intelligence is a single thing that any one animal, or robot, can have more or less of* - which is also very doubtful.

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*An observation made by an anonymous reviewer of one of my papers, for which I am very grateful.

New experiments in embodied evolutionary swarm robotics

My PhD student Paul has started a new series of experiments in embodied evolution in the swarm robotics lab. Here's a picture showing his experiment with 3 Linux e-puck robots in a small circular arena together with an infra-red beacon (at about 2 o'clock).



The task the robots are engaged in collective foraging for food. Actually there's nothing much to see here because the food items are virtual (i.e. invisible) blobs in the arena that the robots have to 'find', then 'pick up' and 'transport' to the nest (again virtually). The nest region is marked by the infra-red beacon - so the robots 'deposit' the food items in the pool of IR light in the arena just in front of the beacon. The reason we don't bother making physical food items and grippers, etc, is that this would entail engineering work that's really not important here. You see, here we are not so interested in collective foraging - it's just a test problem for investigating the thing we're really interested in, which is embodied evolution.

The point of the experiment is this: at the start the robots don't know how to forage for food; during the experiment they must collectively 'evolve' the ability to forage. Paul is here researching the process of collective evolution. Before explaining what's going on 'under the hood' of these robots, let me give some background. Evolutionary robotics has been around for nearly 20 years. The idea is that instead of hand-designing the robot's control system we use an artificial process inspired by Darwinian evolution, called a genetic algorithm. It's really a way of automating the design. Evolutionary algorithms have been shown to be a very efficient way of searching the so-called design space and, in theory, will come up with (literally evolve) better solutions than we can invent by hand. Much more recent is the study of evolutionary swarm robotics (which is why there's no Wikipedia entry yet), which tackles the harder problem of evolving the controllers for individual robots in a swarm such that, collectively, the swarm will self-organise to solve the overall task.

Still with me? Good. Now let me explain what's going on in the robots of Paul's experiment. Each robot has inside it a simulation of itself and it's environment (food, other robots and the nest). That simulation is not run once, but many times over within a genetic algorithm inside the robot. Thus each robot is running a simulation of the process of evolution, of itself, in itself. When that process completes (about once a minute), the best performing evolved controller is transferred into the real robot's controller. Since the embodied evolutionary process runs through several (simulated) generations of robot controller, then the final winner of each evolutionary competition is, in effect, a great great... grandchild of the robot controller at the start of each cycle. While the real robots are driving around in the arena foraging (virtual) food and returning it to the nest, simulated evolution is running - in parallel - as a background process. Every minute or so the real robot's controllers are updated with the latest generation of (hopefully improved) evolved controllers so what we observe is the robots gradually getting better and better at collective foraging. If you think this sounds complicated – it is. The software architecture that Paul has built to accomplish this is ferociously complex and all the more remarkable because it fits within a robot about the size of a salt shaker. But in essence it is like this: what’s going on inside the robots is a bit like you imagining lots of different ways of riding a bike over and over, inside your head, while actually riding a bike.

Putting a simulation inside a robot is something roboticists refer to as ‘robots with internal models’ and if we are to build real-world robots that are more autonomous, more adaptable – in short smarter, this kind of complexity is something we will have to master.

If you’ve made it this far, you might well ask the question, “what if the simulation inside the robot is an inaccurate representation of the real world – won’t that mean the evolved controller will be rubbish?” You would be right. One of the problems that has dogged evolutionary robotics is known as the 'reality gap'. It is the gap between the real world and the simulated world, which means that a controller evolved (and therefore optimised) in simulation typically doesn't work very well - or sometimes not at all - when transferred to the real robot and run in the real world. Paul is addressing this hard problem by also evolving the embedded simulators at the same time as evolving the robot's controllers; a process called co-evolution. This is where having a swarm of robots is a real advantage: just as we have a population of simulated controllers evolving inside each robot, we have a population of simulators - one per robot - evolving collectively across the swarm.

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Related blog posts:
Environment-driven distributed evolutionary adaptation
Walterian creatures

Open Science: from good intentions to hesitant reality

At the start of the Artificial Culture project we made a commitment to an Open Science approach. Actually translating those good intentions into reality has proven much more difficult than I had expected. But now we've made a start, and interestingly the open science part of this research project is turning into a project within a project.

So what's the story? Well, firstly we didn't really know what we meant by open science. We were, at the start, motivated by two factors. One, a strong sense that open science is a Good Thing. And, second, a rather more pragmatic idea that the project might be helped through having a pool of citizen scientists who would help us with interpretation of the results. We knew that we would generate a lot of data and also believed we would benefit from fresh eyes looking over that data, uncoloured - as we are - by the weight of hypotheses and high expectations. We thought we could achieve this simply by putting the whole project, live - as it happens - on the web.

Sounds simple: put the whole project on the web. And now that I put it like this, hopelessly naive. Especially given that we had not budgeted for the work this entails. So, this became a DIY activity fitted into spare moments using free Web tools, in particular Google Sites.

We started experimental work, in earnest, in March 2010 - about two and a half years into the project (building the robots and experimental infrastructure took about two years). Then, by July 2010 I started to give some thought to uploading the experimental data to the project web. But it took me until late October to actually make it happen. Why? Well it took a surprising amount of effort to figure out the best way of structuring and organising the experiments, and the data sets from those experiments, together with the structure of the web pages on which to present that data. But then even when I'd decided on these things I found myself curiously reluctant to actually upload the data sets. I'm still not sure why that was. It's not as if I was uploading anything important, like Wikileaks posts. Perhaps it's because I'm worried that someone will look at the data and declare that it's all trivial, or obvious. Now this may sound ridiculous but posting the data felt a bit like baring the soul. But maybe not so ridiculous given the emotional and intellectual investment I have in this project.

But, having crossed that hurdle, we've made a start. There are more data sets to be loaded (the easy part), and a good deal more narrative to be added (which takes a deal of effort). The narrative is of course critical because without it the data sets are just meaningless numbers. To be useful at all we need to explain (starting at the lowest level of detail):

1. what each of the data fields in each of the data files in each data set means;
2. the purpose of each experimental run: number of robots, initial conditions, algorithms, etc;
3. the overall context for the experiments, including the methodology and the hypotheses we are trying to test.

I said at the start of this blog post that the open science has become a project within a project and happily this aspect is now receiving the attention it deserves: yesterday project co-investigator Frances Griffiths spent the day in the lab here in Bristol, supported by Ann Grand (whose doctoral project is on the subject of Open Science and Public Engagement).

Will anyone be interested in looking inside our data, and - better still - will we realise our citizen science aspirations? Who knows. Would I be disappointed if no-one ever looks at the data? No, actually not. The openness of open science is its own virtue. And we will publish our findings confident that if anyone wants to look at the data behind the claims or conclusions in our papers they can.

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Postscript: See also Frances Griffiths' blog post Open Science and the Artificial Culture Project

On optimal foraging, cod larvae and robot vacuum cleaners

On Monday I took part in a meeting of the Complex Systems Dynamics (CoSyDy) network in Warwick. The theme of the meeting was Movement in models of mathematical biology, and I heard amazing talks about (modelling) albatross flight patterns, e-coli locomotion, locust swarming and the spread of epidemics. (My contribution was about modelling an artificial system - a robot swarm.) Although a good deal of the maths was beyond me, I was struck by a common theme of our talks that I'll try and articulate in this blog post.

The best place to start is by (badly) paraphrasing a part of Jon Pitchford's brilliant description of optimal foraging strategies for cod larvae. Cod larvae, he explained, feed on patches of plankton. They are also very small and if the sea is turbulent the larvae have no chance of swimming in any given direction (i.e. toward a food patch), so the best course of action is to stop swimming and go where the currents take you. Of course the food patches also get washed around by the current so the odds are good that the food will come to you anyway. There's no point wasting energy chasing a food patch. Only if the sea is calm is it worthwhile for the cod larvae to swim toward a food patch. Thus, swim (toward food) when the sea is calm, but don't swim when it's rough, is the optimal foraging strategy for the cod larvae.

It occurred to me that there's possibly a direct parallel with robot vacuum cleaners, like the Roomba.  A robot vacuum cleaner is also foraging, not for food of course, but dirt in the carpet. For the robot vacuum cleaner the equivalent of a rough, turbulent, sea is a room with chaotically positioned furniture. The robot doesn't need a fancy strategy for covering the floor: it just drives ahead and every time it drives up to a wall or piece of furniture it stops to avoid a collision, makes a random turn and drives off again in a straight line. This is the robot's best strategy for reasonable coverage (and hence cleaning) of the floor in a chaotic environment (i.e. a normal room). Only if the room was relatively large and empty (i.e. a calm sea) would the robot (like the cod larvae) need a more sophisticated strategy for optimal cleaning - such as moving in a pattern across the whole area to try and find all the dirt.

Robot vacuum cleaners, like cod larvae, can exploit the chaos in their environment and hence get away with simple (i.e. stupid) foraging strategies. I can't help wondering - given the apparently unpredictable current economic environment - if there's really no point governments or individuals trying to invent sophisticated economic strategies. Perhaps the optimal response to economic turbulence is the KISS principle.

Why large robot swarms (and maybe also multi-cellular life) need immune systems.

Just gave our talk at DARS 2010, basically challenging the common assumption that swarm robot systems are highly scalable by default. In other words the assumption that if the system works with 10 robots, it will work just as well with 10,000. As I said this morning "sorry guys, that assumption is seriously incorrect. Swarms with as few as 100 robots will almost certainly not work unless we invent an active immune system for the swarm". The problem is that the likelihood that some robots partially fail - in other words fail in such a way as to actually hinder the overall swarm behaviour - quickly increases with swarm size. The only way to deal with this - and hence build large swarms - will be to invent a mechanism that enables good robots to both identify and disable partially failed robots. In other words an immune system.

Actually - and this is the thing I really want to write about here - I think this work hints toward an answer to the question "why do animals need immune systems?". I think it's hugely interesting that evolution had to invent immune systems very early in the history of multi-cellular life. I think the basic reason for this might be the very same reason - outlined above - that we can't scale up from small to huge (or even moderately large) robot swarms without something that looks very much like an immune system. Just like robots, cells can experience partial failures: not enough failure to die, but enough to behave badly - badly enough perhaps to be dangerous to neighbouring cells and the whole organism. If the likelihood of one cell failing in this bad way is constant, then it's self-evident that its much more likely that some will fail in this way in an organism with 10,000 cells than 10 cells. And with 10 million cells (still a small number for animals) it becomes a certainty.

Here is the poster version of our paper.

New video of 20 evolving e-pucks

In June I blogged about Nicolas Bredeche and Jean-Marc Montanier working with us in the lab to transfer their environment-driven distributed evolutionary adaptation algorithms to real robots, using our Linux extended e-pucks. Nicolas and Jean-Marc made another visit in August to extend the experiments to a larger swarm size, of 20 robots; they made a YouTube movie and here it is:



In the narrative on YouTube Nicolas writes

This video shows a fully autonomous artificial evolution within a population of ~20 completely autonomous real (e-puck) robots. Each robot is driven by its "genome" and genomes are spread whenever robots are close enough (range: 25cm). The most "efficient" genomes end up being those that successfully drive robots to meet with each other while avoiding getting stuck in a corner.

There is no human-defined pressure on robot behavior. There is no human-defined objective to perform.

The environment alone puts pressure upon which genomes will survive (ie. the better the spread, the higher the survival rate). Then again, the ability for a genome to encode an efficient behavioral strategy first results from pure chance, then from environmental pressure.

In this video, you can observe how going towards the sun naturally emerges as a good strategy to meet/mate with other (it is used as a convenient "compass") and how changing the sun location affect robots behavior.

Note: the 'sun' is the static e-puck with a white band around it.

Twitter

Well, I can't believe I'm on Twitter: https://twitter.com/alan_winfield

Not at all sure I understand what I'm doing yet. There' some puzzling terminology to learn - what's retweeting for instance..? (It sounds like a word from The Meaning of Liff.)

The reason I joined is because I wanted to respond to the questions on @scienceexchange. The first question is

Given the rate of discovery of exo-planets - is there still any doubt that we are not alone in the universe?

And my twittered answer:

Depends: life maybe a little more probable; intelligent life still highly improbable, see Drake's equation

I like the challenge of trying to construct a useful answer in 140 characters.

Google robot car: Great but proving the AI is safe is the real challenge

Great to read that Google are putting some funding into driverless car technology with the very laudable aims of reducing robot traffic fatalities and reducing carbon emissions. Google have clearly assembled a seriously talented group led by Stanford's Sebastian Thrun. (One can only imagine the Boardroom discussions in the car manufacturers this week on Google's entry into their space.)

While this is all very good, I think it's important to keep the news in perspective. Driverless cars have been in development for a long time and what Sebastian has announced this weekend is not a game changing leap forward. To be fair his blog post's main claim is the record for distance driven but Joe Wuensche's group at University BW Munich has a remarkable record of driverless car research; fifteen years ago their Mercedes 500 drove from Munich to Denmark on regular roads, at up to 180 km/h, with surprisingly little manual driver intervention (about 5%). I've seen MuCAR-3, the latest autonomous car from Joe's group, in action in the European Land Robotics Challenge and it is deeply impressive - navigating its way through forest tracks with no white lines or roadside kerbs to help the car's AI figure out where the road's edges are.

So the technology is pretty much there. Or is it?

The problem is that what Thrun's team at Google, and Wuensche's team at UBM, have compellingly demonstrated is proof of principle: trials under controlled conditions with a safety driver present (somewhat controversially at ELROB, because the rules didn't allow a safety driver). That's a long way from your granny getting into her car which then autonomously drives her to the shops without her having to pay attention in case she needs to hit the brakes when the car decides to take a short cut across the vicar's lawn. The fundamental unsolved problem is how to prove the safety and dependability of the Artificial Intelligence (AI) driving the car. This is a serious problem not just for driverless cars, but all next-generation autonomous robots. Proving the safety of a system, i.e. proving that it will both always do the right thing and never do the wrong thing, is very hard right now for conventional systems that have no learning in them (i.e. no AI). But with AI the problem gets a whole lot worse: the AI in the Google car, to quote "becomes familiar with the environment and its characteristics", i.e. it learns. And we don't yet know how to prove the correctness of systems that learn.

In my view that is the real challenge.

Can robots be Three Laws safe?

I'm with about 25 people in a hotel in the New Forest to talk about the ethical, legal and societal issues around robotics. We are a diverse crew: a core of robotics and AI folk, richly complemented by academics in psychology, law, ethics, philosophy, culture, performance and art history. This joint EPSRC/AHRC workshop was an outcome of a discussion on robot ethics at the EPSRC Societal Issues Panel in November 2009. (See also my post The Ethical Roboticist.)

Of course in any discussion about robot ethics it is inevitable that Asimov's Three Laws of Robotics will come up and, I must admit, I've always insisted that they have no value whatsoever. They were, after all, a fictional device for creating stories with dramatic moral ambiguities - not a serious attempt to draw up a moral code of robots. Today I've been forced to revise that opinion. Amazingly we have succeeded in drafting a new set of five 'laws', not for robots themselves but for designers and operators of robots. (You can't have laws for robots because they are not persons - or at least not for the foreseeable future.)

I can't post them here just yet - a joint statement needs to be drafted and agreed first. But to answer the question in the title of this post - no, robots can't be Three Laws Safe, but they quite possibly could be Five Laws Compliant.

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Postscript: here is a much better description of the workshop on Lilian Edwards' excellent blog.

Robot imitation as a method for modelling the foundations of social life

Robot imitation as a method for modelling the foundations of social life: a meeting of robotics and sociology to explore the spread of behaviours through mimesis

Here is the video, posted earlier this month by Frances Griffiths on YouTube, of the meeting of robotics and sociology I blogged about on 21st June. No need for me to write anything more - Roger Stotesbury's excellent 10 minute film explains the whole thing...

Morphogenetic Engineering at ANTS

I'm at the excellent Swarm Intelligence conference in Brussels, called appropriately ANTS. This morning there is a special session on morphogenetic engineering, chaired by René Doursat, of the complex systems institute in Paris. Morphogenetic engineering is the name coined for a new cross over between biology and engineering. Current engineered systems are designed and 'built'. Biological systems on the other hand grow from seeds or embryos. Morphogenetic engineering asks the question, might it be possible to 'grow' complex engineered systems, like robots?

Of course with current materials: metal and plastic, we can't grow robots so many of the ideas of morphogenetic engineering remain, for the time being, future concepts. But I think we'll see some exciting developments in this new sub-field as new materials become available.

Here is an image from our talk* on autonomous distributed morphogenesis in the Symbrion project, presented during the special session. Here you see robots being recruited to join the 2D planar organism during its formation.



* Wenguo Liu and Alan FT Winfield, 'Autonomous morphogenesis in self-assembling robots using IR-based sensing and local communications', ANTS 2010.

Darn - conference paper soundly rejected

As someone who believes in - and from time-to-time advocates - the Open Science approach, I need to practise what I preach. That means being open about the things that don't go according to plan in a research project - including when papers that you think are really great get rejected following peer review. So, let me 'fess up. A paper I submitted to the highly regarded conference Distributed Autonomous Robotic Systems, describing results from the Artificial Culture project, has just been soundly rejected by the reviewers.

Of course, having papers rejected is not unusual. And, like most academics, I tend to react with indignation ("how dare they"), dismissal ("the reviewers clearly didn't understand the work") and embarrassment (hangs head in shame). After a day or two the first two feelings subside, but the embarrassment remains. None of us likes it when our essays come back marked C-. That is why this blog post is not especially comfortable to write.

My paper had four anonymous reviews, and each one was thorough and thoughtful. And - although not all reviewers recommended rejection - the overall verdict to reject was, in truth, fully justified. The paper, titled A Multi-robot Laboratory for Experiments in Embodied Memetic Evolution failed to either fully describe the laboratory, or the experiments. Like most conference papers there was a page limit (12 pages) and I tried to fit too much into the paper.

So, what next for this paper? Well the work will not be wasted. We shall revise the paper - taking account of the reviewers comments - and submit it elsewhere. So, despite my embarrassment, I am grateful to those reviews (I don't know who you are but if you should read this blog - thank you!).

And for Open Science. Well, a fully paid-up card carrying Open Scientist would publish here the original paper and the reviews. But it seems to me improper to publish the reviews without first getting the reviewers' permission - and I can't do that because I don't know who they are. And I shouldn't post the paper either, since to do so would compromise our ability to submit the same work (following revision) somewhere else. So Open Science, even with the best of intentions, has its hands tied by publications protocols.