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Welcome, thank you for the invitation. It's always a pleasure for me to talk about organic computing.

It's also always an opportunity to, or well, this means I'm forced to think about topics and to present them,

especially the topic which is important right now is where do we go,

because we have had this special priority program in the last six years ending last fall,

and we are in the middle of the discussion where to go.

There will be a workshop in December of this year where we will start a new brainstorming process to define new directions,

because organic computing is not at the end.

And so I will talk about the future, but in order to do that, I have to start with just a few remarks on what organic computing is and what we have achieved so far.

This will be very, very short because I assume especially here at this Lehrstuhl it's quite well known and also some considerable progress has been made in this respect.

So I will concentrate here on the second part, and the first thing is looking back,

what did we expect in the beginning and what have we achieved?

And some things have changed, perspective has changed, so it would be a pity after six years to say we have not learned anything.

So things have changed a little bit, and from these considerations we will think about possible future directions.

There are probably more than that. I hope in the workshop in December we will come up with many new ideas.

So there are two things, the one I call social organic computing.

I will emphasize this a little bit because we are working in this area already.

And the other one is the theme of design time to run time.

This is something I think we will hear about later this afternoon, but this will also be very short.

Now what is organic computing?

The starting point more or less was already in the early 1990s when Mark Weiser drew this nice picture,

and he said, well, the size of computers is going down, the price is going down, which means that the number of computers increases.

And this was in his eyes quite positive perspective.

Well, in a sense it is, but at the same time it's also critical because now we have all these many computers everywhere around us.

Most of them are embedded, they are invisible, and looking at this side we have a complexity problem.

How can we manage the complexity of technical systems?

Now there is a very complex system around us, and this works quite well.

It's incredibly stable, has been stable for the last couple of billion years.

We might be able to ruin that in the next few years, but still it is stable.

And it's characterized by certain properties like we have large populations of autonomous systems, subsystems, they interact with each other,

they perceive the environment locally and also act locally with some exceptions, but mainly it's locality.

They are adaptive and able to learn.

They have a long time learning mechanism which we call evolution.

They are self-organized and they develop somehow magically more or less some higher level patterns, some order,

which is exemplified here in this little example which is a petri dish from the top where a chemical reaction is taking place.

These are so-called dissipative structures, strictly local reactions between these molecules and what emerges are these beautiful patterns.

Now what does it mean for technical systems? This is what we are of course interested in.

Now could we apply these natural principles to technical systems?

And if we look at this list of characteristics, then we can state that we have still in technical systems large populations,

growing populations of autonomous agents, they interact with each other and they do it predominantly on a local basis,

but of course exceptions are possible.

So all that we can tick off, but then there are some properties which we normally don't find in technical systems,

and this is adaptivity and learning, this is evolution, this is self-organization.

So we have to look into these mechanisms and understand them, and there is emergence,

and there is a question mark because I'm not always sure if we want to have emergence in technical systems because emergence can be positive and negative,

and we want to have positive emergence only, so is it possible to exclude negative emergence?

So this is still a question mark.

So this is the starting point, and we have about 10 years ago in these first organic computing workshops,

we have then defined the organic computing systems to be robust and flexible because this is the main property we want to see in our systems,

and we identified these properties as lifelike, and this is then translated into all these self-ex properties like self-organization,