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The computational potential of articial living systems can be studied without knowing
the algorithms that govern their behavior. Modeling single organisms by means of so-
called cognitive transducers, we will estimate the computational power of AL systems by
viewing them as conglomerates of such organisms. We describe a scenario in which an
artificial living (AL) system is involved in a potentially infinite, unpredictable interaction
with an active or passive environment, to which it can react by learning and adjusting
its behaviour. By making use of sequences of cognitive transducers one can also model
the evolution of AL systems caused by `architectural' changes. Among the examples are
`communities of agents', i.e. by communities of mobile, interactive cognitive transducers.
Most AL systems show the emergence of a computational power that is not present at
the level of the individual organisms. Indeed, in all but trivial cases the resulting systems
possess a super-Turing computing power. This means that the systems cannot be simulated
by traditional computational models like Turing machines and may in principle solve non-
computable tasks. The results are derived using non-uniform complexity theory
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