Handbook of theoretical computer science (vol. B)
Why interaction is more powerful than algorithms
Communications of the ACM
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
Relativistic Computers and Non-uniform Complexity Theory
UMC '02 Proceedings of the Third International Conference on Unconventional Models of Computation
Interactive Computation: The New Paradigm
Interactive Computation: The New Paradigm
Turing machines, transition systems, and interaction
Information and Computation
Nanomachine computing by quorum sensing
Computation, cooperation, and life
Communications of the ACM
The computational power of interactive recurrent neural networks
Neural Computation
The expressive power of analog recurrent neural networks on infinite input streams
Theoretical Computer Science
Information and Computation
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Classical models of computation no longer fully correspond to the current notions of computing in modern systems. Even in the sciences, many natural systems are now viewed as systems that compute. Can one devise models of computation that capture the notion of computing as seen today and that could play the same role as Turing machines did for the classical case? We propose two models inspired from key mechanisms of current systems in both artificial and natural environments: evolving automata and interactive Turing machines with advice. The two models represent relevant adjustments in our apprehension of computing: the shift to potentially non-terminating interactive computations, the shift towards systems whose hardware and/or software can change over time, and the shift to computing systems that evolve in an unpredictable, non-uniform way. The two models are shown to be equivalent and both are provably computationally more powerful than the models covered by the old computing paradigm. The models also motivate the extension of classical complexity theory by non-uniform classes, using the computational resources that are natural to these models. Of course, the additional computational power of the models cannot in general be meaningfully exploited in concrete goal-oriented computations.