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.
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