The representational entity in physical computing
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The representational entity in physical computing Susan Stepney1
•
Viv Kendon2
Accepted: 1 September 2020 Ó The Author(s) 2020
Abstract We have developed abstraction/representation (AR) theory to answer the question ‘‘When does a physical system compute?’’ AR theory requires the existence of a representational entity (RE), but the vanilla theory does not explicitly include the RE in its definition of physical computing.Here we extend the theory by showing how the RE forms a linked complementary model to the physical computing model. We show that the RE does not need to be a human brain, by demonstrating its use in the case of intrinsic computing in a non-human RE: a bacterium. Keywords Unconventional computing Bacterial computing Representation
1 Introduction Many and diverse physical substrates are proposed for unconventional computing, from relativistic and quantum systems to chemical reactions and slime moulds, from carbon nanotubes to non-linear optical reservoir systems, from amorphous substrates to highly engineered devices, from general purpose analogue computers to one-shot devices.In another domain, biological systems are often said to perform information processing. In all these cases it is crucial to be able to determine when such substrates and systems are specifically computing, as opposed to merely undergoing the physical processes of that substrate. In order to address this question, we have been developing abstraction/representation theory (AR theory). This is a framework in which science, engineering/technology, computing, and communication/signalling are all defined as a form of representational activity, requiring the fundamental use of the representation relation linking physical
This is an extended journal version of Stepney and Kendon (2019), a UCNC 2019 conference paper, including new examples of mental arithmetic and of extrinsic bacterial computing, a new discussion of various modelling issues, and several clarifications throughout. & Susan Stepney [email protected] 1
Department of Computer Science, University of York, York, UK
2
Department of Physics, Durham University, Durham, UK
system and abstract model in order to define their operation (Horsman et al. 2014; Horsman 2015). Within this framework, it is possible to distinguish scientific experimentation on a novel substrate (an activity necessary to characterise the computational capabilities of a substrate) from the performance of computation by that substrate. This is needed to distinguish cases where a substrate superficially appears to be computing, because it sometimes produces a state that resembles a computational result (which can be determined only by comparison with a separately computed result), from cases where a substrate is reliably and consistently producing desired computational results. In work following on from the original definitions, Horsman et al. (2017b) provide a high level overview, Horsman et al. (2018) delve into more philosophical aspects, and
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