Structural complexity 1
The emperor's new mind: concerning computers, minds, and the laws of physics
The emperor's new mind: concerning computers, minds, and the laws of physics
Handbook of theoretical computer science (vol. B)
Circuits of the mind
Why interaction is more powerful than algorithms
Communications of the ACM
Journal of the ACM (JACM)
Pulsed neural networks
Simulating the mind: a gaunlet thrown to computer science
ACM Computing Surveys (CSUR)
How we know what technology can do
Communications of the ACM
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
Turing
Towards Algorithmic Explanation of Mind Evolution and Functioning
MFCS '98 Proceedings of the 23rd International Symposium on Mathematical Foundations of Computer Science
Beyond the Turing Limit: Evolving Interactive Systems
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
Some connections between nonuniform and uniform complexity classes
STOC '80 Proceedings of the twelfth annual ACM symposium on Theory of computing
How We Think of Computing Today
CiE '08 Proceedings of the 4th conference on Computability in Europe: Logic and Theory of Algorithms
On evolutionary lineages of membrane systems
WMC'05 Proceedings of the 6th international conference on Membrane Computing
Defining and detecting emergence in complex networks
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
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The information processing capabilities of artificial living (AL) systems are far more powerful than commonly believed. Modelling single organisms by means of so-called cognitive transducers, we estimate the computational power of AL systems by viewing them as conglomerates of organisms. We describe a scenario in which an AL system is engaged in a potentially unbounded, unpredictable interaction with an environment, to which it can react by learning and adjusting its behaviour. By means of lineages of cognitive transducers we also model the evolution of AL systems. Among the examples are "communities of agents", i.e., 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 derive from recent results from the theory of interactive evolutionary computing systems in terms of AL systems.